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Awareness Programmes for the inscription of Tripitaka to be a World Heritage
The Consulate General of Sri Lanka in Toronto in co-ordination with the Buddhist temples in Toronto organized a series of events to mark the inscription of Tripitaka to be a World Heritage.
On Saturday, 16th March 2019, the first event was organized at the West End Buddhist Temple & Meditation Centre in Mississauga. The programme commenced with the observance of the five precepts followed by the Buddha Pooja. This was followed by two lectures which were conducted during the event on 'Introduction to Tripitaka' by Ven. Kusalagnana Thero and on 'Thripitaka and Spiritual values of Sri Lankan Social Life' by Dr. Sarath Chandrasekera of the University of Toronto.
During the event, the Consul General explained the scope of the programme and briefed the importance of the inscription of Tripitaka to be a world heritage and invited Sri Lankans living in the Greater Toronto Area and its suburbs to support towards inscription of Tripitaka to be a World Heritage.
At the conclusion of the programmes Ven. Maha Sangha were presented with pirikara and Dr. Sarath Chandrasekera was given a token of appreciation.
The second programme was held at the Brampton Buddhist Mission Centre in Caledon on Sunday, 17th March 2019, which commenced by Dhamma discussion followed by alms giving with the participation of Sri Lankans living in Brampton.
The concluding ceremony was held at the Toronto Maha Vihara on Sunday, 24th March 2019. On this occasion alms giving was offered for Maha Sangha and to the devotees followed by Dhamma Sermon on the Thripitaka by Ven. Ahangama Rathanasiri Nayake Thero, Toronto Maha Vihara and Ven. Sasana Sarana Thero of Mahamewnawa Bhavana Asapuwa.
Consulate General of Sri Lanka
02 April 2019


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Crochet Gauge Ruler
£4.65 From £4.65
Now it has become really simple to measure the gauge of your crocheted project. Take this ruler, lay it on top of your gauge and find the line on the ruler where the stitches match the stitches of your crocheted project underneath. It's that easy!
Paula Rowan
Great idea, but be careful when you open this. It arrived in an envelope inside the main parcel, I cut the top off the envelope with scissors and cut into the ruler! It is quite thin and a bit flimsy

subset: commoncrawl


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Are you unknowingly raising a spoiled child? Watch out for these 5 signs
You might be raising a spoiled child without you even noticing! Read on to find out more.
Obviously, no parent wants to raise a spoiled or entitled child. That's why it's important for parents to know the types of behavior that can unknowingly turn their beloved child into a spoiled brat.
1. Constantly giving them rewards
Rewarding your child for good grades, or doing chores, is generally okay. However, if it seems that you're rewarding them for every little good deed, then you might want to rethink your parenting style.
Rewards should be given sparingly, since in some cases, their success in class, or winning a competition is the reward in and of itself.
2. Giving them too much allowance
Your kids' allowance should be mainly used to buy food, or pay for the commute whenever they go to school. Their allowance shouldn't be a salary or wage.
Just give them enough for their daily needs, and a little extra that they can save up, if they choose to do so. Teach your child to value every peso that they have, and you'll have kids who'll be more appreciative and less spoiled.
3. Acting spoiled yourself
It's important for parents to teach their kids through their own examples, especially when it comes to discipline. Try to reassess your behavior and habits, and think if anything you're doing might make your children spoiled or entitled.
It's also a good way to take a look at yourself, and figure out how you can be better as a person, and as a parent.
4. Not being smart with your own spending
When it comes to spending habits, your kids will learn a lot from your own habits. So it's important to teach them at a young age to learn how to budget their money, and spend only within their means, as well as the importance of saving up their money.
Your bad spending habits is definitely something that your kids can acquire from you, so it's best to change your behavior early on.
Teach them to be practical when it comes to buying things, and let them understand the difference between their wants, and their needs.
5. Not teaching them to save
While your kids are still young, they'll be depending on you for all of their financial needs. Once they grow up, they'll generally be responsible for the money that they will be earning.
That's why it's important to teach your kids to save their money at a very young age, so that they can have the discipline to not splurge or spend their money on things that they won't need.
It's also important for parents to not always 'bail out' their kids if they run out of money. Don't act like your an ATM or a credit card to your kids. Help them to an extent, but make them understand the error of their mistakes if they overspend.
Source: time.com
Republished with permission from: theAsianParent Philippines


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About binkyboots
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- Birthday 04/24/1980
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so far favorites are... alecto O scarecrow carnivale sloth black rose umbra jolly roger civet is good, honey and leather... incense, rose but only if it's deep and dark
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binkyboots replied to wickedgoddess's topic in Black Phoenix Trading Postooh, this is fabulous! sharp/sweet/herbal/musky and clean all at the same time.... it's as though dorian took you out and rolled you in the herb garden
lavender, dude, and lots of it! then this kind of green/lushness underneath, I dont get the eucalyptus though.... or anise. mike and I both tried this last night, I slept, like a baby for once, except for a very disturbing house fire dream mike is still sleeping now! but I'm thinking his regular medication has more to do with that, lol. scent wise, it's very relaxing, soothing, smells clean and comforting. didn't last very long, mostly I can still smell whatever perfume I put on before bed the next morning, but nothing, like a dream scent!
Golden Priapus
binkyboots replied to RosieP's topic in Ars Amatoriaraar! this is gorgeous, quite possibly my favourite bpal so far... I love that medicinal juniper/pine scent, it's one of my favourite notes, so mix that up with sweet golden vanilla, god yes! I can see the amped up snake oil comparisons, but this is way better. it (strangely) reminds me of carmex lipbalm, not a bad thing, just odd! lasted for ages and transferred to my scarf, yum
Antique Lace
binkyboots replied to shriekingviolet's topic in Discontinued Scentsoh my... I've been so looking forward to trying this, and I'm not dissapointed! it's delicious, creamy, sweet and musky, with a pretty strong throw (this isn't a fragile little thing!) It doesn't change much on me, but that's a good thing. another "cold dead hands" bottle (pry it from them!) I wonder if it changes as it ages....
Pink Phoenix
binkyboots replied to GypsyRoseRed's topic in Limited Editionsooh.. post shower scent review! pink phoenix, man is it ever pink! I like it, I'm just not sure it's me, I get a lot of vanilla and honey (good!!) but there's the strawberry poking it's head in. The strawbery takes over a bit for a while, but the vanilla is still there. I think it might be too girlie on me but I'm going to try a few more times as I adore the honey/vanilla/mild fruitiness I get at first.
Dove's Heart
binkyboots replied to viciousviolet's topic in Discontinued Scentsok... my hormones are out of whack at the moment, but here goes... I cant pick out the notes, it's soft and sweet, florals, I thought I was smelling rose and herbs but ymmv, like a blanket wrapped around my shoulders, utterly comforting. I think this will go in my next order.
Snow Moon
binkyboots replied to Macha's topic in A Little LunacyI ought to just learn, cold, snowy notes and I are bad, very bad. it starts out piney, but then turns sour piney... I dont mind the initial few minutes, it's just this sour smell I get that causes me to run screaming for some soap. on my sister it goes entirely the opposite way, pine turning to musky sweet florals with a forest lurking in the backgroud.
Dana O'Shee
binkyboots replied to shriekingviolet's topic in Bewitching Brewsin the bottle, this smells like heaven, it's almonds, cream and honey, like porridge (lol, I'm a scot, forgive me!) with double cream and honey melting on top of it. on me, hey! where did it go? I smell, nothing, dang. later (post gym workout) woah, who's got the marzipan, hand it over... oh, it's me. wierd, I really would love to wear thing but I think I might use up my imp in a shower scrub.
binkyboots replied to Rheliwen's topic in Bewitching Brewsavast! smells like buttered rum lifesavers, only with a shot of malibu on the side, this you must understand is not a bad thing. I was a little worried, boozy scents normally dont like me, but this is beyond gorgeous. it's really lovely and has a good throw...
binkyboots replied to rosered's topic in Black Phoenix Trading PostA sweet reward for a worthy deed: caramelized sugar and sweet cream. I'm not at all sure about this one, it's sweet buttery caramel goodness, but maybe too much? it almost smells burnt to me, but I'll give it another go just in case, I'd like to love it, buttery, caramel type scents and I are usually
binkyboots replied to shriekingviolet's topic in Black Phoenix Trading Postit's like mint buttercream on vanilla cake!! I adore snowblind, it's not very strong on me, but I swear you'll have to pry this bottle from my cold dead hands! it's just, buttermint, that's all I get, the best, most wonderful minty bpal ever.
Sexy, Smutty, Seductive, Provocative... It's All in Here
binkyboots replied to ipb's topic in Recommendationsgrog! and snake oil
binkyboots replied to Ina Garten Davita's topic in Sin & SalvationI've put off and put off trying dorian, tea is (unless I have a head cold) not my thing, the idea of smelling like vanilla tea, bleh. but hey, it was sitting there in my imp stash.... oh man, I need this stuff, I'll bath in it, wear it, knit a scarf inspired by it.... I get vanilla, sugared lemons, tea, a little spice (but understated) and musk, full on glorious musk. this is so going in my next order.
binkyboots replied to shriekingviolet's topic in Bewitching Brewsit's cinnamon, lots of it, and honey? it's got a really sweet slightly creamy drydown on me and lasts, a few hours, not bad
binkyboots replied to Lilithlefay's topic in Limited EditionsI was so excited to get a chance to try this, I'm a taurean and quite typical of the sign I think at any rate... first sniff, I get, turmeric? definately curry, how odd. on trying it the curry faded (thankfully) and it turned deeper, like mossy loam, wet and maybe a little rotten (but not in a bad way!) I like the dirty smell I'm getting, it's strong too. no eucalyptus here, but that's ok, like a previous poster said it warms up to a sweet earthy scent, nice for layering with lush's rasoul mud (previously middle earth)


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Scottish Provident With Profits
RWP Research Findings, Overview and Comments
In considering any company and its with-profits RYWP will look to assess a range of factors which will include: the company's management record; its transparency of information, its financial strength; its prospects for providing future returns and the nature of its with-profits approach.
In this regard we believe that Phoenix run some of the poorest with profits funds in the UK today. The Scottish Provident fund has been one of the better performers within this overall mix. However it is better than some truly appalling ones, the comparison is therefore against a low benchmark.
In many respects although their funds are called with-profits, they are run in a similar way to a traditional managed fund. They have lost contact with the basic ethos which originally stood behind the with-profits concept and made it a popular and successful investment. Policyholders are now in funds which more often than not will have poor performance and limited future prospects.
Overall our view of Phoenix is that their performance is poor as is their future return prospects.
We believe we can give the Scottish Provident fund a moderate rating; it is not as bad as some of the other funds in the Phoenix 'stable'. Overall its record and position is better than most of the other Phoenix with-profits funds, but judged as an investment against many alternatives it remains a 'questionable' investment. Any judgement call must ultimately be based on an individual policy by policy basis; there can be reasons to stay or reasons to exit a policy, which can only be determined by that individual policy's position and that of the policyholder. This judgement can only be made by looking at the specific detail and information.
For our specific report on Phoenix Life which will give you all the information you need to judge their funds please click here
Company ownership position
Scottish Provident no longer exists as a stand-alone company, its with-profits is now housed as a closed fund under Phoenix Life.
Phoenix Life and Phoenix Life Assurance Limited are part of Phoenix Group Holdings, a FTSE 250 Company. They operate 13 with-profits funds (10 under the Phoenix Life banner, 3 under the Phoenix Assurance Limited banner) which are all closed to new business. They also operate two non-profit funds. Phoenix is the UK's largest specialist consolidator of closed life funds. Their funds were all closed to new business by their previous owners, in many cases after experiencing difficulties.
Scottish Provident With Profits Fund
The Phoenix Life Limited SPI With Profits Fund consists of former Scottish Provident Limited SPI Fund policies. This fund has an estimated value of £3.1 million.
The annual bonuses on this fund in the past couple of years have not been too bad compared to other Phoenix with-profits – 3-4% on unitised with-profits.
Their shareholders received a share of traditional with-profits bonuses which amounted to £19.3 million in 2013. Not knowing the split between traditional/unitised with-profits it is difficult for us to estimate the return on their funds, but it looks like this is in the 3-4% range across the board.
Please remember that any of our summary findings or conclusions are general; an appraisal of the general position for that company. There will be situations where an individual policyholder with a company that is 'good'' may want to look at surrendering or transferring their policy (for example to secure a guarantee or because it does not suit their risk position), likewise there will be situations where policyholders with a 'poor' company want to stick with what they have (for example because the penalty of exiting will be too great). Nothing can replace getting a report based on your unique circumstances; it is only this unique, personalised report which will provide the information that will instruct you on your circumstances regardless of the company and our general comments.
Click here to get request more detail from us about Scottish Provident


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- Introduction to Machine Learning
- Model and Cost Function
- Gradient Decent
- Multivariate Linear Regression
- Classification
- Logistic Regression Model
- Overfitting & Regularization
- Neural Networks
- Back Propagation
- Back Propagation in Practice
- Evaluating a Learning Algorithm
- Bias vs. Variance
- Precision and Recall
Introduction to Machine Learning
What is Machine Learning?
Two definitions of Machine Learning are offered. Arthur Samuel described it as:
"the field of study that gives computers the ability to learn without being explicitly programmed." This is an older, informal definition.
Tom Mitchell provides a more modern definition:
"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."
Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game.
In general, any machine learning problem can be assigned to one of two broad classifications:
Supervised learning and Unsupervised learning.
Supervised Learning
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into "regression" and "classification" problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
Example 1:
Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
We could turn this example into a classification problem by instead making our output about whether the house "sells for more or less than the asking price." Here we are classifying the houses based on price into two discrete categories.
Example 2:
(a) Regression- Given a picture of a person, we have to predict their age on the basis of the given picture
(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
So it actually have two kind of problems:
Regression: to predict a specific value
classification: to predict 0/1 problems
When the target variable that we're trying to predict is continuous, such as in our housing example, we call the learning problem a regression problem. When y can take on only a small number of discrete values (such as if, given the living area, we wanted to predict if a dwelling is a house or an apartment, say), we call it a classification problem.
Unsupervised Learning
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning there is no feedback based on the prediction results.
Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
Non-clustering: The "Cocktail Party Algorithm", allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party). ** Unsupervised learning does not predict the specific value or property, but instead it categories the data set, for example distinct all the circle from all the rectangle, or your voice from a mixed voice. **
Model and Cost Function
Model Representation
To establish notation for future use, we'll use x(i) to denote the "input" variables (living area in this example), also called input features, and y(i) to denote the "output" or target variable that we are trying to predict (price). A pair (x(i), y(i)) is called a training example, and the dataset that we'll be using to learn—a list of m training examples (x(i), y(i)); i=1 ,…, m - is called a training set. Note that the superscript "(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. We will also use X to denote the space of input values, and Y to denote the space of output values. In this example, X = Y = ℝ.
To describe the supervised learning problem slightly more formally, our goal is, given a training set, to learn a function h : X → Y so that h(x) is a "good" predictor for the corresponding value of y. For historical reasons, this function h is called a hypothesis. Seen pictorially, the process is therefore like this:
Cost Function
We can measure the accuracy of our hypothesis function by using a cost function. This takes an average difference (actually a fancier version of an average) of all the results of the hypothesis with inputs from x's and the actual output y's.
To break it apart, it is ½ x¯ where x¯ is the mean of the squares of hθ(xi) - yi , or the difference between the predicted value and the actual value.
This function is otherwise called the "Squared error function", or "Mean squared error". The mean is halved (½) as a convenience for the computation of the gradient descent, as the derivative term of the square function will cancel out the ½ term. The following image summarizes what the cost function does:
If we try to think of it in visual terms, our training data set is scattered on the x-y plane. We are trying to make a straight line (defined by hθ(x)) which passes through these scattered data points.
Our objective is to get the best possible line. The best possible line will be such so that the average squared vertical distances of the scattered points from the line will be the least. Ideally, the line should pass through all the points of our training data set. In such a case, the value of J(θ0, θ1) will be 0. The following example shows the ideal situation where we have a cost function of 0.
When θ1=1, we get a slope of 1 which goes through every single data point in our model. Conversely, when θ1=0.5, we see the vertical distance from our fit to the data points increase.
This increases our cost function to 0.58. Plotting several other points yields to the following graph:
Thus as a goal, we should try to minimize the cost function. In this case, θ1=1 is our global minimum.
A contour plot is a graph that contains many contour lines. A contour line of a two variable function has a constant value at all points of the same line. An example of such a graph is the one to the right below.
Taking any color and going along the 'circle', one would expect to get the same value of the cost function. For example, the three green points found on the green line above have the same value for J(θ0, θ1) and as a result, they are found along the same line. The circled x displays the value of the cost function for the graph on the left when θ0 = 800 and θ1= -0.15. Taking another h(x) and plotting its contour plot, one gets the following graphs:
When θ0 = 360 and θ1 = 0, the value of J(θ0, θ1) in the contour plot gets closer to the center thus reducing the cost function error. Now giving our hypothesis function a slightly positive slope results in a better fit of the data.
The graph above minimizes the cost function as much as possible and consequently, the result of θ1 and θ0 tend to be around 0.12 and 250 respectively. Plotting those values on our graph to the right seems to put our point in the center of the inner most 'circle'.
Note: How to read this plot: The contour plot is actually a 3-D graph, and the center point is the lowest whereas the outside is higher. Consider as a bowl. And what we need is to find the lowest point, in this case (θ1, θ0) = (0.12, 250) ***
Gradient Decent
Now we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That's where gradient descent comes in.
Imagine that we graph our hypothesis function based on its fields θ0 and θ1 (actually we are graphing the cost function as a function of the parameter estimates). We are not graphing x and y itself, but the parameter range of our hypothesis function and the cost resulting from selecting a particular set of parameters.
We put θ0 on the x axis and θ1 on the y axis, with the cost function on the vertical z axis. The points on our graph will be the result of the cost function using our hypothesis with those specific theta parameters. The graph below depicts such a setup.
We will know that we have succeeded when our cost function is at the very bottom of the pits in our graph, i.e. when its value is the minimum. The red arrows show the minimum points in the graph.
The way we do this is by taking the derivative (the tangential line to a function) of our cost function. The slope of the tangent is the derivative at that point and it will give us a direction to move towards. We make steps down the cost function in the direction with the steepest descent. The size of each step is determined by the parameter α, which is called the learning rate.
For example, the distance between each 'star' in the graph above represents a step determined by our parameter α. A smaller α would result in a smaller step and a larger α results in a larger step. The direction in which the step is taken is determined by the partial derivative of J(θ0,θ1). Depending on where one starts on the graph, one could end up at different points. The image above shows us two different starting points that end up in two different places.
The gradient descent algorithm is:
repeat until convergence:
where j = 0,1 represents the feature index number.
At each iteration j, one should simultaneously update the parameters θ1, θ2,…, θn. Updating a specific parameter prior to calculating another one on the j(th) iteration would yield to a wrong implementation.
we explored the scenario where we used one parameter θ1 and plotted its cost function to implement a gradient descent. Our formula for a single parameter was :
Repeat until convergence:
Regardless of the slope's sign for d/dθ1J(θ1), θ1 eventually converges to its minimum value. The following graph shows that when the slope is negative, the value of θ1 increases and when it is positive, the value of θ1 decreases.
On a side note, we should adjust our parameter α to ensure that the gradient descent algorithm converges in a reasonable time. Failure to converge or too much time to obtain the minimum value imply that our step size is wrong.
How does gradient descent converge with a fixed step size α?
The intuition behind the convergence is that d/dθ1J(θ1) approaches 0 as we approach the bottom of our convex function. At the minimum, the derivative will always be 0 and thus we get:
θ1 := θ1 - α*0
When specifically applied to the case of linear regression, a new form of the gradient descent equation can be derived. We can substitute our actual cost function and our actual hypothesis function and modify the equation to :
where m is the size of the training set, θ0 a constant that will be changing simultaneously with θ1 and xi ,yi are values of the given training set (data).
The point of all this is that if we start with a guess for our hypothesis and then repeatedly apply these gradient descent equations, our hypothesis will become more and more accurate.
So, this is simply gradient descent on the original cost function J. This method looks at every example in the entire training set on every step, and is called batch gradient descent. Note that, while gradient descent can be susceptible to local minima in general, the optimization problem we have posed here for linear regression has only one global, and no other local, optima; thus gradient descent always converges (assuming the learning rate α is not too large) to the global minimum. Indeed, J is a convex quadratic function. Here is an example of gradient descent as it is run to minimize a quadratic function.
The ellipses shown above are the contours of a quadratic function. Also shown is the trajectory taken by gradient descent, which was initialized at (48,30). The x's in the figure (joined by straight lines) mark the successive values of θ that gradient descent went through as it converged to its minimum.
Multivariate Linear Regression
Multiple Features
Linear regression with multiple variables is also known as "multivariate linear regression".
We now introduce notation for equations where we can have any number of input variables.
The multivariable form of the hypothesis function accommodating these multiple features is as follows:
h(x) = θ0 + θ1x1 + θ2x2 + θ3x3 + ⋯ + θnxn
In order to develop intuition about this function, we can think about θ0 as the basic price of a house, θ01/sub> as the price per square meter, θ2 as the price per floor, etc. x1 will be the number of square meters in the house, x2 the number of floors, etc.
Using the definition of matrix multiplication, our multivariable hypothesis function can be concisely represented as:
This is a vectorization of our hypothesis function for one training example; see the lessons on vectorization to learn more.
Remark: Note that for convenience reasons in this course we assume x0(i) = 1 for (i∈1,…,m). This allows us to do matrix operations with θ and x. Hence making the two vectors 'θ' and x(i) match each other element-wise (that is, have the same number of elements: n+1).]
Gradient Descent For Multiple Variables
The gradient descent equation itself is generally the same form; we just have to repeat it for our 'n' features:
in other words:
The following image compares gradient descent with one variable to gradient descent with multiple variables:
Feature Scaling
We can speed up gradient descent by having each of our input values in roughly the same range. This is because θ will descend quickly on small ranges and slowly on large ranges, and so will oscillate inefficiently down to the optimum when the variables are very uneven.
The way to prevent this is to modify the ranges of our input variables so that they are all roughly the same. Ideally:
−1 ≤ x(i) ≤ 1
−0.5 ≤ x(i) ≤ 0.5
These aren't exact requirements; we are only trying to speed things up. The goal is to get all input variables into roughly one of these ranges, give or take a few.
Two techniques to help with this are feature scaling and mean normalization. Feature scaling involves dividing the input values by the range (i.e. the maximum value minus the minimum value) of the input variable, resulting in a new range of just 1. Mean normalization involves subtracting the average value for an input variable from the values for that input variable resulting in a new average value for the input variable of just zero. To implement both of these techniques, adjust your input values as shown in this formula:
xi := (xi - μi) / si
Where μi is the average of all the values for feature (i) and si is the range of values (max - min), or si is the standard deviation.
Note that dividing by the range, or dividing by the standard deviation, give different results. The quizzes in this course use range - the programming exercises use standard deviation.
For example, if xi represents housing prices with a range of 100 to 2000 and a mean value of 1000, then,
xi := (price − 1000)/1900.
Learning Rate
Debugging gradient descent. Make a plot with number of iterations on the x-axis. Now plot the cost function, J(θ) over the number of iterations of gradient descent. If J(θ) ever increases, then you probably need to decrease α.
Automatic convergence test. Declare convergence if J(θ) decreases by less than E in one iteration, where E is some small value such as 10−3. However in practice it's difficult to choose this threshold value.
It has been proven that if learning rate α is sufficiently small, then J(θ) will decrease on every iteration.
To summarize:
If α is too small: slow convergence.
If α is too large: may not decrease on every iteration and thus may not converge.
Polynomial Regression
Our hypothesis function need not be linear (a straight line) if that does not fit the data well.
We can change the behavior or curve of our hypothesis function by making it a quadratic, cubic or square root function (or any other form).
For example, if our hypothesis function is hθ(x)=θ0+θ1x1 then we can create additional features based on x1, to get the quadratic function hθ(x)=θ0+θ1x1+θ2x12 or the cubic function hθ(x)=θ0+θ1x1+θ2x12+θ3x13 In the cubic version, we have created new features x2 and x3 where x2=x12 and x3=x13.
To make it a square root function, we could do: hθ(x)=θ0+θ1x1+θ2*√x1 One important thing to keep in mind is, if you choose your features this way then feature scaling becomes very important.
eg. if x1 has range 1 - 1000 then range of x12 becomes 1 - 1000000 and that of x13 becomes 1 - 1000000000
Normal Equations
Gradient descent gives one way of minimizing J. Let's discuss a second way of doing so, this time performing the minimization explicitly and without resorting to an iterative algorithm. In the "Normal Equation" method, we will minimize J by explicitly taking its derivatives with respect to the θj 's, and setting them to zero. This allows us to find the optimum theta without iteration. The normal equation formula is given below:
There is no need to do feature scaling with the normal equation.
The following is a comparison of gradient descent and the normal equation:
With the normal equation, computing the inversion has complexity O(n3). So if we have a very large number of features, the normal equation will be slow. In practice, when n exceeds 10,000 it might be a good time to go from a normal solution to an iterative process.
To attempt classification, one method is to use linear regression and map all predictions greater than 0.5 as a 1 and all less than 0.5 as a 0. However, this method doesn't work well because classification is not actually a linear function.
The classification problem is just like the regression problem, except that the values we now want to predict take on only a small number of discrete values. For now, we will focus on the binary classification problem in which y can take on only two values, 0 and 1. (Most of what we say here will also generalize to the multiple-class case.) For instance, if we are trying to build a spam classifier for email, then x(i) may be some features of a piece of email, and y may be 1 if it is a piece of spam mail, and 0 otherwise. Hence, y∈{0,1}. 0 is also called the negative class, and 1 the positive class, and they are sometimes also denoted by the symbols "-" and "+." Given x(i), the corresponding y(i) is also called the label for the training example.
Hypothesis Representation
We could approach the classification problem ignoring the fact that y is discrete-valued, and use our old linear regression algorithm to try to predict y given x. However, it is easy to construct examples where this method performs very poorly. Intuitively, it also doesn't make sense for hθ(x) to take values larger than 1 or smaller than 0 when we know that y ∈ {0, 1}. To fix this, let's change the form for our hypotheses hθ(x) to satisfy 0≤hθ(x)≤1. This is accomplished by plugging θTx into the Logistic Function.
Our new form uses the "Sigmoid Function," also called the "Logistic Function":
The following image shows us what the sigmoid function looks like:
The function g(z), shown here, maps any real number to the (0, 1) interval, making it useful for transforming an arbitrary-valued function into a function better suited for classification.
hθ(x) will give us the probability that our output is 1. For example, hθ(x)=0.7 gives us a probability of 70% that our output is 1. Our probability that our prediction is 0 is just the complement of our probability that it is 1 (e.g. if probability that it is 1 is 70%, then the probability that it is 0 is 30%).
Decision Boundary
In order to get our discrete 0 or 1 classification, we can translate the output of the hypothesis function as follows:
The way our logistic function g behaves is that when its input is greater than or equal to zero, its output is greater than or equal to 0.5:
g(z)≥0.5 when z≥0
So if our input to g is θTX, then that means:
hθ(x) = g(θTx) ≥ 0.5
when θTx ≥ 0
From these statements we can now say:
θTx ≥ 0 ⇒ y = 1
θTx < 0⇒ y = 0
The decision boundary is the line that separates the area where y = 0 and where y = 1. It is created by our hypothesis function.
In this case, our decision boundary is a straight vertical line placed on the graph where x1=5, and everything to the left of that denotes y = 1, while everything to the right denotes y = 0.
Again, the input to the sigmoid function g(z) (e.g. θTX) doesn't need to be linear, and could be a function that describes a circle (e.g. z=θ0+θ1x21+θ2x22) or any shape to fit our data.
Logistic Regression Model
Cost Function
We cannot use the same cost function that we use for linear regression because the Logistic Function will cause the output to be wavy, causing many local optima. In other words, it will not be a convex function.
Instead, our cost function for logistic regression looks like:
When y = 1, we get the following plot for J(θ) vs hθ(x):
Similarly, when y = 0, we get the following plot for J(θ) vs hθ(x):
In summary:
If our correct answer 'y' is 0, then the cost function will be 0 if our hypothesis function also outputs 0. If our hypothesis approaches 1, then the cost function will approach infinity.
If our correct answer 'y' is 1, then the cost function will be 0 if our hypothesis function outputs 1. If our hypothesis approaches 0, then the cost function will approach infinity.
Note that writing the cost function in this way guarantees that J(θ) is convex for logistic regression.
Simplified Cost Function and Gradient Descent
We can compress our cost function's two conditional cases into one case:
Cost(hθ(x),y)= −y log(hθ(x))−(1−y)log(1−hθ(x))
Notice that when y is equal to 1, then the second term (1−y)log(1−hθ(x)) will be zero and will not affect the result. If y is equal to 0, then the first term −y log(hθ(x)) will be zero and will not affect the result.
We can fully write out our entire cost function as follows:
A vectorized implementation is:
Remember that the general form of gradient descent is:
We can work out the derivative part using calculus to get:
Notice that this algorithm is identical to the one we used in linear regression. We still have to simultaneously update all values in theta.
A vectorized implementation is:
Multiclass Classification: One-vs-all
Now we will approach the classification of data when we have more than two categories. Instead of y = {0,1} we will expand our definition so that y = {0,1…n}.
Since y = {0,1…n}, we divide our problem into n+1 (+1 because the index starts at 0) binary classification problems; in each one, we predict the probability that 'y' is a member of one of our classes.
We are basically choosing one class and then lumping all the others into a single second class. We do this repeatedly, applying binary logistic regression to each case, and then use the hypothesis that returned the highest value as our prediction.
The following image shows how one could classify 3 classes:
To summarize:
Train a logistic regression classifier hθ(x) for each class to predict the probability that  y = i .
To make a prediction on a new x, pick the class that maximizes hθ(x)
Overfitting & Regularization
The Problem of Overfitting
Consider the problem of predicting y from x ∈ R. The leftmost figure below shows the result of fitting a y = θ0+θ1x to a dataset. We see that the data doesn't really lie on straight line, and so the fit is not very good.
Instead, if we had added an extra feature x2 , and fit y=θ0+θ1x+θ2x2 , then we obtain a slightly better fit to the data (See middle figure). Naively, it might seem that the more features we add, the better. However, there is also a danger in adding too many features: The rightmost figure is the result of fitting a 5th order polynomial. We see that even though the fitted curve passes through the data perfectly, we would not expect this to be a very good predictor of, say, housing prices (y) for different living areas (x). Without formally defining what these terms mean, we'll say the figure on the left shows an instance of underfitting—in which the data clearly shows structure not captured by the model—and the figure on the right is an example of overfitting.
Underfitting, or high bias, is when the form of our hypothesis function h maps poorly to the trend of the data. It is usually caused by a function that is too simple or uses too few features. At the other extreme, overfitting, or high variance, is caused by a hypothesis function that fits the available data but does not generalize well to predict new data. It is usually caused by a complicated function that creates a lot of unnecessary curves and angles unrelated to the data.
This terminology is applied to both linear and logistic regression. There are two main options to address the issue of overfitting:
1) Reduce the number of features:
Manually select which features to keep. Use a model selection algorithm (studied later in the course). 2) Regularization
Keep all the features, but reduce the magnitude of parameters θj. Regularization works well when we have a lot of slightly useful features.
Cost Function
If we have overfitting from our hypothesis function, we can reduce the weight that some of the terms in our function carry by increasing their cost.
Say we wanted to make the following function more quadratic:
We'll want to eliminate the influence of θ3x3 and θ4x4 . Without actually getting rid of these features or changing the form of our hypothesis, we can instead modify our cost function:
We've added two extra terms at the end to inflate the cost of θθ3 and θθ4. Now, in order for the cost function to get close to zero, we will have to reduce the values of θ3 and θ4 to near zero. This will in turn greatly reduce the values of θ3x3 and θ4x4 in our hypothesis function. As a result, we see that the new hypothesis (depicted by the pink curve) looks like a quadratic function but fits the data better due to the extra small terms θ3x3 and θ4x4.
This is the main idea about Regularization.
We could also regularize all of our theta parameters in a single summation as:
The λ, or lambda, is the regularization parameter. It determines how much the costs of our theta parameters are inflated.
Using the above cost function with the extra summation, we can smooth the output of our hypothesis function to reduce overfitting. If lambda is chosen to be too large, it may smooth out the function too much and cause underfitting. Hence, what would happen if λ=0 or is too small ?
Regularized Linear Regression
We will modify our gradient descent function to separate out θ0 from the rest of the parameters because we do not want to penalize θ0.
With some manipulation our update rule can also be represented as:
The first term in the above equation, 1−αλ/m will always be less than 1. Intuitively you can see it as reducing the value of θj by some amount on every update. Notice that the second term is now exactly the same as it was before.
Normal Equation
To add in regularization, the equation is the same as our original, except that we add another term inside the parentheses:
L is a matrix with 0 at the top left and 1's down the diagonal, with 0's everywhere else. It should have dimension (n+1)×(n+1). Intuitively, this is the identity matrix (though we are not including x0), multiplied with a single real number λ.
Regularized Logistic Regression
We can regularize logistic regression in a similar way that we regularize linear regression. As a result, we can avoid overfitting. The following image shows how the regularized function, displayed by the pink line, is less likely to overfit than the non-regularized function represented by the blue line:
Recall that our cost function for logistic regression was:
We can regularize this equation by adding a term to the end:
Neural Networks
Model Representation
Let's examine how we will represent a hypothesis function using neural networks. At a very simple level, neurons are basically computational units that take inputs (dendrites) as electrical inputs (called "spikes") that are channeled to outputs (axons). In our model, our dendrites are like the input features x1 ⋯ xn, and the output is the result of our hypothesis function. In this model our x0 input node is sometimes called the "bias unit." It is always equal to 1. In neural networks, we use the same logistic function as in classification, 1/1+e−θTx, yet we sometimes call it a sigmoid (logistic) activation function. In this situation, our "theta" parameters are sometimes called "weights".
Visually, a simplistic representation looks like:
Our input nodes (layer 1), also known as the "input layer", go into another node (layer 2), which finally outputs the hypothesis function, known as the "output layer".
We can have intermediate layers of nodes between the input and output layers called the "hidden layers."
In this example, we label these intermediate or "hidden" layer nodes a20⋯a2n and call them "activation units."
If we had one hidden layer, it would look like:
The values for each of the "activation" nodes is obtained as follows:
This is saying that we compute our activation nodes by using a 3×4 matrix of parameters. We apply each row of the parameters to our inputs to obtain the value for one activation node. Our hypothesis output is the logistic function applied to the sum of the values of our activation nodes, which have been multiplied by yet another parameter matrix Θ(2) containing the weights for our second layer of nodes.
Each layer gets its own matrix of weights, Θ(j).
The dimensions of these matrices of weights is determined as follows:
If network has sj units in layer j and sj+1 units in layer j+1, then Θ(j) will be of dimension sj+1 × (sj+1). The +1 comes from the addition in Θ(j) of the "bias nodes," x0 and Θ(j)0. In other words the output nodes will not include the bias nodes while the inputs will. The following image summarizes our model representation:
Back Propagation
Cost Function
Let's first define a few variables that we will need to use:
- L = total number of layers in the network
- sl = number of units (not counting bias unit) in layer l
- K = number of output units/classes
Recall that in neural networks, we may have many output nodes. We denote hΘ(x)k as being a hypothesis that results in the kth output. Our cost function for neural networks is going to be a generalization of the one we used for logistic regression. Recall that the cost function for regularized logistic regression was:
For neural networks, it is going to be slightly more complicated:
We have added a few nested summations to account for our multiple output nodes. In the first part of the equation, before the square brackets, we have an additional nested summation that loops through the number of output nodes.
In the regularization part, after the square brackets, we must account for multiple theta matrices. The number of columns in our current theta matrix is equal to the number of nodes in our current layer (including the bias unit). The number of rows in our current theta matrix is equal to the number of nodes in the next layer (excluding the bias unit). As before with logistic regression, we square every term.
- the double sum simply adds up the logistic regression costs calculated for each cell in the output layer
- the triple sum simply adds up the squares of all the individual Θs in the entire network.
- the i in the triple sum does not refer to training example i
Back Propagation Algorithm
"Backpropagation" is neural-network terminology for minimizing our cost function, just like what we were doing with gradient descent in logistic and linear regression. Our goal is to compute:
That is, we want to minimize our cost function J using an optimal set of parameters in theta. In this section we'll look at the equations we use to compute the partial derivative of J(Θ):
To do so, we use the following algorithm:
Back Propagation Intuition
Back Propagation in Practice
Gradient Checking
Random Initialization
Initializing all theta weights to zero does not work with neural networks. When we backpropagate, all nodes will update to the same value repeatedly. Instead we can randomly initialize our weights for our Θ matrices using the following method:
Putting it Together
First, pick a network architecture; choose the layout of your neural network, including how many hidden units in each layer and how many layers in total you want to have.
- Number of input units = dimension of features x(i)
- Number of output units = number of classes
- Number of hidden units per layer = usually more the better (must balance with cost of computation as it increases with more hidden units)
- Defaults: 1 hidden layer. If you have more than 1 hidden layer, then it is recommended that you have the same number of units in every hidden layer.
Training a Neural Network
- Randomly initialize the weights
- Implement forward propagation to get hΘ(x(i)) for any x(i)
- Implement the cost function
- Implement backpropagation to compute partial derivatives
- Use gradient checking to confirm that your backpropagation works. Then disable gradient checking.
- Use gradient descent or a built-in optimization function to minimize the cost function with the weights in theta.
When we perform forward and back propagation, we loop on every training example:
for i = 1:m,
Perform forward propagation and backpropagation using example (x(i),y(i))
(Get activations a(l) and delta terms d(l) for l = 2,...,L
The following image gives us an intuition of what is happening as we are implementing our neural network:
Ideally, you want hΘ(x(i)) ≈ y(i). This will minimize our cost function. However, keep in mind that J(Θ) is not convex and thus we can end up in a local minimum instead.
Evaluating a Learning Algorithm
Evaluating a Hypothesis
Once we have done some trouble shooting for errors in our predictions by:
- Getting more training examples
- Trying smaller sets of features
- Trying additional features
- Trying polynomial features
- Increasing or decreasing λ
We can move on to evaluate our new hypothesis.
A hypothesis may have a low error for the training examples but still be inaccurate (because of overfitting). Thus, to evaluate a hypothesis, given a dataset of training examples, we can split up the data into two sets: a training set and a test set. Typically, the training set consists of 70 % of your data and the test set is the remaining 30 %.
The new procedure using these two sets is then:
- Learn Θ and minimize Jtrain(Θ) using the training set
- Compute the test set error Jtest(Θ)
Model Selection and Train/Validation/Test Sets
Just because a learning algorithm fits a training set well, that does not mean it is a good hypothesis. It could over fit and as a result your predictions on the test set would be poor. The error of your hypothesis as measured on the data set with which you trained the parameters will be lower than the error on any other data set.
Given many models with different polynomial degrees, we can use a systematic approach to identify the 'best' function. In order to choose the model of your hypothesis, you can test each degree of polynomial and look at the error result.
One way to break down our dataset into the three sets is:
- Training set: 60%
- Cross validation set: 20%
- Test set: 20%
We can now calculate three separate error values for the three different sets using the following method:
- Optimize the parameters in Θ using the training set for each polynomial degree.
- Find the polynomial degree d with the least error using the cross validation set.
- Estimate the generalization error using the test set with Jtest(Θ(d)), (d = theta from polynomial with lower error);
This way, the degree of the polynomial d has not been trained using the test set.
Bias vs. Variance
Diagnosing Bias vs. Variance
In this section we examine the relationship between the degree of the polynomial d and the underfitting or overfitting of our hypothesis.
We need to distinguish whether bias or variance is the problem contributing to bad predictions. High bias is underfitting and high variance is overfitting. Ideally, we need to find a golden mean between these two. The training error will tend to decrease as we increase the degree d of the polynomial.
At the same time, the cross validation error will tend to decrease as we increase d up to a point, and then it will increase as d is increased, forming a convex curve.
High bias (underfitting): both Jtrain(Θ) and JCV(Θ) will be high. Also, JCV(Θ)≈Jtrain(Θ).
High variance (overfitting): Jtrain(Θ) will be low and JCV(Θ) will be much greater than Jtrain(Θ).
The is summarized in the figure below:
Regularization and Bias/Variance
In the figure above, we see that as λ increases, our fit becomes more rigid. On the other hand, as λ approaches 0, we tend to over overfit the data. So how do we choose our parameter λ to get it 'just right' ? In order to choose the model and the regularization term λ, we need to:
- Create a list of lambdas (i.e. λ∈{0,0.01,0.02,0.04,0.08,0.16,0.32,0.64,1.28,2.56,5.12,10.24});
- Create a set of models with different degrees or any other variants.
- Iterate through the λs and for each λ go through all the models to learn some Θ.
- Compute the cross validation error using the learned Θ (computed with λ) on the JCV(Θ) without regularization or λ = 0.
- Select the best combo that produces the lowest error on the cross validation set.
- Using the best combo Θ and λ, apply it on Jtest(Θ) to see if it has a good generalization of the problem.
Learning Curves
Training an algorithm on a very few number of data points (such as 1, 2 or 3) will easily have 0 errors because we can always find a quadratic curve that touches exactly those number of points. Hence:
- As the training set gets larger, the error for a quadratic function increases.
- The error value will plateau out after a certain m, or training set size.
Experiencing high bias:
Low training set size: causes Jtrain(Θ) to be low and JCV(Θ) to be high.
Large training set size: causes both Jtrain(Θ) and JCV(Θ) to be high with Jtrain(Θ)≈JCV(Θ).
If a learning algorithm is suffering from high bias, getting more training data will not (by itself) help much.
Experiencing high variance:
Low training set size: Jtrain(Θ) will be low and JCV(Θ) will be high.
Large training set size: Jtrain(Θ) increases with training set size and JCV(Θ) continues to decrease without leveling off. Also, Jtrain(Θ) < JCV(Θ) but the difference between them remains significant.
If a learning algorithm is suffering from high variance, getting more training data is likely to help.
Precision and Recall


text:
March 27th at 8:41am
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A swaddled baby is a happy baby!!!
My little owl friend again just haggin out
Notice the shirt with the expression!!
Can anyone see Morgan??? Where is she?
I love falling asleep in my swing now. (of course with it locked, I am getting ready to take her out) :)


text:
look, sometimes i think deep thoughts. this particular breed of thought has been floating around in my brain a good long while now. and i don't think i'll quite cover it in one blog. because parts of it have a tendency to be quite ranty, and it's weekend time and i'm all mellow and whatnot.
it's hard to be a human sometimes. it's harder still to be a happy human; there are always going to be those times where you feel like shit and have a strong suspicion that goes alongside this feeling that you, in fact, look like shit. unhappy people, or let's be honest, me when i am an unhappy person (because that's the only real authority with which i feel comfortable to speak), tends to be a bit down on my levels of self-awesome.
ok, and to add to that, it is my opinion that humans are not meant to be happy, as far as this wonderful world full of shiny things is concerned. after all, if you are happy and fulfilled, why would you buy a footspa, a wafflemaker, books you may never read, unlimited brooches from etsy? and then where would we all be? jobless, homeless, cold. this is my very basic understanding of capitalism. i stubbornly refuse to look into fleshing this explanation out on account of an ex-boyfriend who has made an academic career out of things surrounding and pertaining to this very question (he was against it, but it didn't stop him enjoying the waffles, did it?).
there are plenty of things in the world out there that are geared towards making us feel we're not enough - we're not smart enough, we're not fast enough, we're not rich enough, we're not thin enough, we're not pretty enough (ask kasey chambers). but the hint is that perhaps if we bought some more stuff, maybe then we might be a little happier.
i'm completely guilty of all of this. i'm somebody who opshops when their room is already filled with things. i like shiny stuff, i cannot lie. and i own a lot of clothes. like, a lot. and books ... well. at some point my brain processed the thought that books = safety. and so now i line my walls with them, almost literally. i figure at very least they're insulation and maybe kindling if it comes to that... you know, when the world ends.
but i digress. if we never feel ok with ourselves we'll keep spending our money. and truthfully, i don't think humans can be happy all the time, but we could probably work on being a little less unhappy.
the main way in which i've been noticing and processing the unhappiness of humans is our connection to our physical body. man oh man, that's a tricky one. as far as my own physical experience allows as a woman, yeah, hella tricky. note how carefully i don't say women have it worse than men. i guess it's a tricky for each individual as we allow it to be.
NB: this is a huge turnaround for me - i'm a feminist at heart, but have been working a lot on making distinctions between my own experience and those of others. that way, people can hopefully identify with things i say as being a reality of my life, and i'm not ranting too much. i may end up ranting though. i have some opinions, in a most human-like way.
recently, i've been noticing increasingly how life is a push to always be better, faster, fitter (more productive), thinner, stronger. strength and betterness i can get behind. since i was probably 11 or 12, i've been well aware of that push. it comes from everywhere, and of course let's blame the media, whomever they are, as well as the things and the stuff. but really, i wanted to go grassroots here and suggest we do it ourselves (DIY is so hot right now) or we help each other out with maybe taking a bit of control over the monolith of Push.
the Push to be beautiful. beauty. what a notion. it's powerful but it's completely subjective. it's been invented over and over. why does it matter what anyone else calls beautiful when we are the ones that have to live with ourselves?
self-help, kids. what i mean by 'beautiful is not skin deep' is that we can decide on our own, if we want. and then we can go out and be beautiful. and not push, unless we want to push. perhaps beautiful is not the most sensical word to apply to a man, but i think you're picking up what i'm putting down here - perhaps it's just taking a good long look at what motivates you to be yourself, what bits fill you with joy, and which bits are dark and bruised and tender.
for me, being a beautiful version of myself is doing what i want to do, as often as i can. checking in to make sure the means to my end are worthy. you're right, it's pretty easy to write and feel from a place of happy, which is where i am now (generally; most days).
in a physical way, my mentality has developed over years of hate and discomfort to this: be healthy; be strong; smile at yourself in the mirror (even if there are pudgy bits hanging over the top of your pants), get sweaty and love it; eat food; if you want chocolate, eat it and enjoy it and then continue about your day without regret or mental flagellation; do squats; enjoy the day-after muscle ache with a tinge of pride; be proud of your good decisions and forgiving of your mistakes and moments of veering from your planned path. just stop saying negative things about yourself; if there are things you want to work on and it helps you tell someone your goal to keep you on the path, tell them, then do the work required. propel yourself forward right into your life.
"You don't have to be what other people want you to be. You don't have
to be interesting or agreeable or entertaining. You don't have to tone
yourself down, quiet your voice, or hide your feelings. You don't have
to be outgoing or spontaneous or sociable. You don't have to be thin or
beautiful or anyone's definition of
attractive. You don't have to be anyone other than who you authentically
are, and you sure as hell don't have to spend your time and energy
trying to convince people that you're worth keeping around. The right
people are going to recognize your worth. They are going to respect you,
appreciate you, and accept you, without forcing you to compromise who
you are. Life is too short, and your happiness is far too important, to
make room for anyone who treats you otherwise." ~ Daniell Koepke
i know it's not easy. to figure out how to be happy and then to be it, that's an undertaking worthy of a whole life. beautiful is not just skin deep. it goes beyond your bones. ignore those things out there that make you feel like you're not worthy, or that you will only be happy when you get to a size or weight or shape. ignore as hard as you can, and when you have a slip and accidentally listen to all those things out there, go back to the mirror and smile at yourself. think a little on how pretty your eyes are, take yourself out for dinner and a movie, reset ready for the next morning.
the unhealthy decisions we make are always guilty, always over-do it, always confirm the whisperings of that nasty voice telling us we don't matter enough to take care of. go for a walk listening a song like Ben Howard's cover of "Call Me Maybe" and think some deep thoughts, or put on "Waiting all Night" by Rudimental featuring Ella Eyre. and just boogy. my logic here is to move your blood around til it's warm and happy again. it's not science, i'll admit. but there is science to back up the whole exercise = endorphins thing. believe it. just don't think that simply exercising will straighten your shit out. this requires careful thought, and you are worthy of the time.
rant over, for now. probably more to come - who knows how much more i might have to say on the subject.
... [if you're] a person of intelligence, a person of integrity, then you're considered a minority in this world ... And it's going to be really hard to find messages of self-love and support anywhere. It's all about how you have to look a certain way or you're worthless. For us to have self-esteem is truly an act of revolution and our revolution is long overdue." ~ Margaret Cho


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May 3-5 | May the Fourth be with you...
Sarah says...
Around town this weekend:
Are there any Star Wars fans in your house? Celebrate "Star Wars Day" on May 4th... "May the fourth be with you!" East Benton County History Museum will be showing Fan Films relating to Star Wars. Seven films will be shown between noon and 4pm. Follow this link for more information. If you are looking for more weekend fun, Tri-Fun staff will be hanging out with Knockerball at the Farmer's Market Pavilion for Cinco de Mayo this weekend, 101-147 S. 4th Street in Pasco. Come and get a free flex-frisbee!
Mother's Day is May 12
You know what makes a great gift? Yep, a Tri-Fun Experience Pass. Let Mom enjoy glass fusing or a glass blowing class at the Glass Punty. Maybe she'd prefer a paint class at Wet Palette, a night out at Joker's? With an Experience Pass, she'll have 19 FREE and fun things to do for $50. Check out our "Date Night" bundle (2 Entertainment Passes and 1 Dining Pass) and plan a year of mom-dates! (She really *just* wants to spend time with you!!) If your Mom is super cool – you can even take her to Red Dot Paintball. Gift cards for Tri-Fun Passes can be purchased on our website or at Arlene's Flowers & Gifts at 1177 Lee Blvd. in Richland. (You might want to grab her favorite flowers too!)
Take your friends or family along this weekend.
You asked, we got 'em. Two brand new Experience Pass venues: Spare Time Lanes and Atomic Bowl! Both offer a free game with rental of those snazzy shoes. (Plus, ALSO free at Spare Time Lanes, you can also try out Thursday night black light bowling.) I'd make a night of it and have a calzone and adult beverage at Folded Pizza Pie. It'll improve my mood after the gutter balls :) Can you break 100 bowling?


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University Health Alliance
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Hawaii Department of Commerce and Consumer Affairs
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Frederic Kusseler
Frederic Kusseler is a Belgian poet you are probably entirely unfamiliar with. This would be due, of course, to the fact that this entry marks his first publication.
Having been introduced to the works of Edgar Alan Poe at the age of twenty, Frederic decided to use the English language exclusively for any literary expression. His long-standing love affair with poetry started when he stumbled across Charles Baudelaire's 'Les Fleurs du Mal' only one year later.
Too erratic to ever achieve any academic titles, Frederic has spent his time working left and right, always keeping the possibility of a literary career firmly in mind. Having been more or less dared into getting the word out by his former English teacher, Frederic instantly recognised Able Muse as his first desired port of call. Et voilà. The result is here, for your enjoyment. Farewell for now. 'On Cruelty' awaits.
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CompleWare, VIDA Diagnostics, UI To Collaborate On Radiology Imaging
Radiology Imaging
CompleWare Corporation, VIDA Diagnostics, University of Iowa plan to collaborate in providing software and services to manage radiological images, such as CT and MRI, for clinical drug trials.
"This is an exciting opportunity that will allow pharmaceutical companies to collect, analyze, manage and report all clinical research data using only one software application," said John Weiler, M.D., CompleWare president and UI professor emeritus of internal medicine. "Companies performing clinical research want all data to be integrated. We call this interoperability. It means that we can now review and study patient diaries, clinical case report forms, spirometry (breathing tests), electrocardiograms, laboratory data and radiological images with one logon to the Internet."
CompleWare offers a full range of products and services to manage clinical research studies, with customers worldwide. VIDA Diagnostics, Inc. provides the ability to automate the review of lung X-ray images using advanced software.
The UI's "Quantitative Imaging of Iowa" (UIQI2) is a new project aimed at offering a multidisciplinary setting to develop imaging protocols. Furthermore, it includes the ability to perform a wide range of imaging studies, offers clinical image review, and helps obtain quantifiable data to assist in analysis of disease severity and treatment response. UIQI2 includes faculty from the UI Roy J. and Lucille A. Carver College of Medicine departments of radiology and internal medicine and the College of Engineering.
"We will work together to provide unique radiological services to the pharmaceutical industry," said Edwin van Beek, M.D., professor of radiology. "We can significantly enhance the information we obtain from imaging studies."
"VIDA software provides automated analyses of lung density and airways that will be very informative in patients with emphysema, asthma and chronic obstructive pulmonary disease," said John Garber, VIDA Diagnostics' chief executive officer. "This three-way collaboration will allow us to commercialize our specialized software and integrate it with CompleWare state-of-the-art applications such as their CompleClinical Suite."
CompleWare Corporation, based in North Liberty, Iowa, focuses on the development and deployment of innovative software applications and services to capture and manage clinical research data such as case report forms, patient reported outcomes, laboratory, pharmacokinetic, quality of life, electrocardiogram, vital sign, spirometry and radiological image data.


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This job has expired, please see additional jobs below
BI Programmer Analyst
Job Details – this job has expired, please see similar jobs below
Work with Company's world class technology to develop, implement, and support Company's global infrastructure.
As a member of the IT organization, assist with the analyze of existing complex programs and formulate logic for new complex internal systems. Prepare flowcharting, perform coding, and test/debug programs. Develop conversion and system implementation plans. Recommend changes to development, maintenance, and system standards.
Job duties are varied and complex utilizing independent judgment. May have project lead role. BS or equivalent experience in programming on enterprise or department servers or systems.
Company is an Affirmative Action-Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, protected veterans status, age, or any other characteristic protected by law.
Company University will soon be implementing a cloud based reporting/analytics platform as a replacement for the current OBIEE solution. This shift will require a redesign of the architecture in order to adapt to the cloud product (Company Analytics Cloud) and the changing complexities of the business. As part of this transition, we are seeking to add strong cloud analytics experience within the BI team who will assume a key role of setting the application architecture and participate in the transition to the cloud.
This role requires a unique set of skills and experience that will need to be strongly aligned to our business model and the applications we use. The ideal candidate would need to have experience implementing cloud based BI solution across a large organization. This role requires he/she be a hybrid that becomes familiar with the details of business processes as well as a deep understanding of the platform capabilities. Lastly, this role requires experience with technology change management through a major system transition.
This initial focus for this role is to become familiar with the business drivers and design/implement the new OU reporting platform on OAC. The overall goal of this new platform is deliver the reporting customers receive today on the cloud system. Longer term this individual will collaborate with partners to map out the future of analytics to match the direction of the business.
- Analyze processes, prioritize areas of improvement, and work alongside key stakeholders to drive change
- Hands on implementation of analytics platform
- Act as platform subject matter expert
- Leverage experience and share skillsets with the team
- Communicate status, project risks, etc. to senior management
- Facilitate meetings
- Produce documentation including status reports, requirements, analysis, etc.
- Work with business groups to translate business needs into technical requirements
- Foster and model a collaborative team environment driven to build great software while having fun
- 7+ years of experience in the Technology and/or Learning space
- Proven experience with BI solution design / architecture
- Track record in a rollout of an analytics solution
- Experience with OBIEE, OACS, ETL tools and related products
- Experience in the learning/education industry is a plus
- Experience with a cloud based BI product would be ideal but not required
- Experience using Agile performance metrics to drive accountability and continuous improvement
- Proven leadership and motivation skills
- Strong written and oral communication skills a must; can present formally or informally
- Can maintain a high level of energy and engagement
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Sometimes you just get obsessed. Sometimes this is flow, the zen state, in the zone, and your work is going well. But sometimes it might just be fascination and the puzzle of whatever you're focused on, but that doesn't get you anywhere. It's a long thread on social media that keeps going in circles. It's day-to-day coverage of politics.
It's rarely necessary, but it's addictive. If it keeps you from working on your thing, it's probably better to treat it like a momentary thought in meditation practice. Notice, then let it go.
It does sound easier than it seems. The secret to meditation practice, though, is that you aren't judging the distraction. You're just noticing it exists. It's okay that it comes back. We're patient.
Acknowledge the obsession, then turn back to the thing you make. Repeat as needed.


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If you're looking for a broad-spectrum multimineral supplement that may work to support promote well-being' you might want to try Kirkman's Advanced Mineral Support.
First' Advanced Mineral Support provides calcium and iron bis-glycinate' which are easy to absorb and gentle on the digestive tract. Calcium may act to maintain strong bones and teeth while also working to support muscle contraction and nerve impulse transmission. On the other hand' iron may be involved in producing the red blood cells that are responsible for transporting oxygen throughout your body.
Furthermore' Kirkman has added trace minerals like chromium' molybdenum' and selenium to this product because these minerals may offer additional support for general health.
Finally' Advanced Mineral Support is hypoallergenic as well as free of artificial preservatives' flavorings' and colorings.
At the standard serving size of two capsules daily' each 180-capsule bottle of Advanced Mineral Support by Kirkman should last for about three months.
These statements have not been evaluated by the Food and Drug Administration (FDA). These products are not meant to diagnose' treat or cure any disease or medical condition. Please consult your doctor before starting any exercise or nutritional supplement program or before using these or any product during pregnancy or if you have a serious medical condition.
Serving Size: 2 Capsules
Amount Per Serving / % DV
Vitamin D (Cholecalciferol) 60 IU / 15%
Calcium (Bis-glycinate) 200mg / 20%
Iron (bis-glycinate) 2mg / 11%
Iodine (potassium iodide) 50mcg / 33%
Magnesium (glycinate' citrate' oxide) 100mg / 25%
Zinc (citrate) 15mg / 100%
Selenium (chelate) 25mcg / 35%
Manganese (citrate) 2.7mg / 135%
Chromium (chelate) 60mcg / 50%
Molybdenum (chelate) 50mcg / 66%
Boron (Chelate) 500mcg / **
Vanadium (chelate) 10mcg / **
*Percent Daily Values based on a 2'000 calorie diet.
**Daily Value not established.
Calcium bis-glycinate' magnesium oxide' plant cellulose' boron chelate' zinc citrate' iron bis-glycinate' manganese citrate' chromium chelate' molybdenum chelate' selenium chelate' magnesium stearate (vegetable)' vitamin D3' vanadium chelate' magnesium citrate' magnesium glycinate' potassium iodide' plant cellulose capsule.
Free Of:
Casein' gluten' soy' yeast' milk' sucrose' preservatives' flavorings' colorings' gelatin' egg' peanuts' tree nuts or fish.
2 capsules daily or as directed by a physician.
Store in a cool dry place.
Keep out of the reach of children.
Allergy / Dietary Needs
- Egg Free
- Fish Free
- Gluten Free
- Hypoallergenic
- Nut Free
- Soy Free
- Starch Free
- Sugar Free
- Wheat Free
- Yeast Free
- Gelatin Free


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We have run out of stock for this item.
Back for the season, Savoy is Xirena's gauze version of your favorite t-shirt. Pair with the Starlyn Short and you're ready for summer.
- Details: Simply pulls on, relaxed fit
- Material: 100% cotton
- Item fits true to size, model wears a size XS
- Machine was cold, lay flat to dry, cool iron if needed
- Made in LA
*expected to ship by July 2, 2020*


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AAM S-Line Short Tail Exhaust Nissan 370Z
- Remove from Your Build List
- Add to Your Build List
Note: To fit the S-Line Short Tail Exhaust properly requires removal of emissions canister shield
The AAM Competition S-Line Short Tail is a brand new axle-back exhaust for the 2009-2010 Nissan 370Z. Built from 304 stainless steel, the S-Line Short Tail replaces the factory rear section, improving exhaust flow and saving approximately 18 lbs in the process. Not bad for an axle-back exhaust. The S-Line ''Shortys'' are perfect for someone looking to dramatically change the sound of their Z without having to break the bank and install a full catback exhaust system. Check out the video below for a sound preview!
Please make sure to fill out a review after you've installed the product. Your review should be your honest opinion, therefore, it can be positive or negative. We DO read your reviews, and relay infomation to manufacturers if a product needs to be changed or has issues.
Please keep in mind that reviews that say "This is a nice product" don't help other customers understand why they should or should not buy the product.
16-Gauge T-304 Stainless Steel; 18lbs Weight Savings over Stock


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Acacia Safaris is one of...

Acacia Safaris is one of Uganda's safari companies specializing in short excursions, extended tours, tailor made safaris, car hire and hotels reservations. We give our customers personalized care and attention. We have capacity to comfortably handle small and large groups. We have intimate knowledge and passion for the country and our success depends on how we share its beauty, wild behavior and culture.

Plot 351b Balintuma Road Nakulabye, Kampala

P.O.Box PO Box 29493, Kampala, Uganda

Tel: +256 (0)414 253597

Fax: +256 (0)414 532742

Mobile: +256 (0)712 800004

E-mail: email@example.com

Website: www.acaciasafari.co.ug

Contact person: Managing Director: Connie Tumusiime

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Filename: views/service_directory_v.php
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After scouting the Nile in 1986 and again in 1995, Adrift made the first ever descent of the Victoria Nile in 1996 and introduced commercial rafting to Uganda. Rafting has since become the most popular tourist activity in the country. Over the past two decades we have explored some of the wildest rivers on the planet and the source of the Nile is our favourite. We now offer thrilling adventures for all ages and abilities. At our Nile High Bungee and Camp on the equator, the mighty Nile rushes rapidly between forest covered islands, explodes in huge warm waves and creates the best rapids for rafting, kayaking and river surfing to be found anywhere in the world. Back to Auto members directory overview listing.
Plot 14, York Terrace Kololo.
P.O.Box 7681, Kampala, Uganda
Tel: +256 312 237 438
Fax: +256 414 252 720
Mobile: +256 772 454 206
E-mail: email@example.com
Website: www.adrift.ug
Contact person: Managing Director: Ntege Denis
Plot 72, Muteesa 2 Rd...
Plot 72, Muteesa 2 Rd Ntinda, Kampala
P.O.Box 21611 Kampala, Uganda - East Africa
Tel: +256-392-949157 / +256-392-949156 / +256-772-863446
Africa Adventure Safaris...

Africa Adventure Safaris is one of the Uganda tour operators offering exciting trips into the deep wilderness of Uganda, Rwanda, Congo, Burundi, Kenya and Tanzania. We offer a long hands on experience in organizing tailor-made wildlife and hospitality safaris at discounted rates.

Seven Sisters House Plot 64, Hoima RD, 1st Floor, appt 3.

P.O.Box 22754, Kampala

Tel: +256 392834606

Mobile: +256 701106004

E-mail: firstname.lastname@example.com

Website: www.ugandagorillatour.com, www.rwandagorillassafari.com

Contact person: Wilberforce Begumisa

KIIRA House (Former...
KIIRA House (Former Colline House)
Plot 4, Pilkington Road, Kampala
Tel: +256 (0) 414 259 172, +256 (0) 414 575 391
Mob: +256 (0) 772 901 801
African Crane Safaris is a...
African Crane Safaris is a local Uganda safari company offering a host of Uganda safaris, Gorilla Tours and Safari holidays taking in all the adventure safari activities as well as leisurely holiday packages in Uganda and the rest of East Africa. Our safari Packages include gorilla trekking in Uganda and Rwanda, wildlife tours – big game drives, mountain trekking / hiking, fishing trips and cultural tours in East Africa and we are advocates of responsible tourism.
Plot 505 Sajuuju Complex, Ground Floor
Entebbe Road
Tel: +256 (0) 414 599 301
Mob: +256 (0) 776 022 802


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Blog Archives
Today is April 22, 2012 and that means it's Earth Day!! What fun things are you doing today and everyday to ensure you are going green? Not sure where to start? Here are some fun facts to help you start living a greener lifestyle today!
- Stop using disposable bag and start using reusable bags. I have SO many of these!! Prices range from FREE – $0.99. This is an awesome way to grocery shop and they are awesome to carry with you on a busy Saturday while running errands.
- Turn off the lights whenever you leave a room.
- Turn your computer completely off at night. When you set your computer to "sleep" at night instead of turning it completely off, you are still using energy. You could save up to 300 watts by making this simple change.
- Start paying your bills online as well and start opting to receive you statements online.
- Unplug chargers and appliances when they are not in use.
- Collect rainwater and use that to water your houseplants and/or garden.
- Use a cloth instead of paper to clean your kitchen. Be creative! Use an old t-shirt or towel instead of a paper towel or sponge.
- Turn the water off while brushing your teeth. Doing this act along will save you 4 gallons of water.
- Shorten your showers. Every minute you cut from your shower is roughly 5 gallons of water!
- Plant a tree.
What things are you doing to go green? Share your tips and tricks below!
I hope you all are enjoying your Sunday! Have an incredible week!
Love Ya!


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Women's Swiss Made Toccata Diamond Bezel Watch
$499 $700
gift box included, water resistant to 50 meters (165 feet), black art ausone leather (synthetic shantung style) strap,100 diamonds on bezel, 34mm stainless steel case, black dial with diamond markers,buckle closure,swiss made
Browse all deals at T.J. Maxx
See more in Watches
* This offer may contain time-sensitive information and offers. Please check with T.J. Maxx to confirm actual price & availability.
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3689 N Vermilion St
T.J. Maxx
3689 N Vermilion St
Danville, IL 61832


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Kendell Lang (http://www.KendellLang.com) is a Social Media Keynote Speaker and Internet Marketing Specialist using proven Information Architecture to creatively and inexpensively find ways to achieve results. An automated social media marketing specialist in successful social network marketing for growth-oriented, Internet-savvy, E-Commerce-enabled, social networking websites. Strategic social media marketing techniques and automated social media broadcasting.
Thursday, June 24, 2010
Importance of Keyword Selection in Social Media Marketing
Social Media Marketing and Social Media Optimization, as well as traditional Search Engine Marketing, are directly impacted by Keyword Selection.
What most clients do not understand about online marketing, website construction, SEO or SMO is Keywords, more specifically Keyword Selection. If we have a chance to, we will be glad to do an analysis of a prospective clients website and see whether the keywords for which they rank actually have any value. If the client doesn't know anything about Keywords, then educating them is likely to identify the pain point they didn't know existed. Specifically, if they see a demonstration that they may be missing out on 80% of highly valuable target traffic (potential new business) for related keywords in their target audience…the awareness of losing that business should get their attention.
Keyword Selection has become a combination of art and science, so in the following outline I will provide the fundamentals to give you a high level understanding of how sophisticated Keyword Selection has become. Most people do not make appropriate keyword selections, so understanding this will help you guide discussions in the future.
The Keyword Selection issue with respect to impact on search engine ranking is four-fold.
• First is whether the keyword is considered a Root Keyword (also called a Primary Keyword) or whether it is a Keyword Stem (also called a Secondary Keyword).
• Second is whether the keyword is "Global" or "Localized" and this means whether the keyword is geographically concentrated on the vicinity around the clients location, i.e. dental implants Houston.
• Third is the Keyword Value. We want our clients to obtain search engine rank for 3 types of keywords, High-Value, Medium-Value, and Low-Value. What you don't want is to rank for keywords which have No-Value.
• Fourth is Keyword Length. Root keywords are single words, like Dentist (very broad). Secondary Keywords include both Keyword Stems and Keyword Phrases (two or more words as part of the keyword). There are "short-tail keyword phrases" (two to four words in the keyword phrase, still fairly broad) and there are "long-tail keyword phrases" (very lengthy and detailed phrases which are highly specific, very narrow). Long-tail keywords are typically competitive niche markets rather than the hugely competitive broad keywords
So, here is the simple example for what all the above looks like:
Root (Global) Keywords = dentist, dentistry, dental,
Keyword Stems (Secondary, short-tail) = dentist office, dentistry practice, dental office
Localized Root (Global) Keywords = dentist houston, dentistry houston, dental houston
Localized Keyword Stems (Secondary, short-tail) = dentist office houston, dentistry practice houston, dental office houston
Long-tail keyword phrase = small business dentist office providing top dentistry practice in houston
Keywords in the above categories are further value gauged by some general "rule-of-thumb" benchmarks:
1. Number of Pages Indexed
2. Number of Monthly Searches
3. Average Cost per Click
If you go to Google and search for "dentist" you will see that Google has indexed approximately 36,300,000 pages of content.
In comparison, if you Google "dentist office houston" you will see that Google has indexed approximately 15,100,000 pages of content, less than half of "dentist" total pages indexed. Further down is the long-tail keyword phrase "small business dentist office providing top dentistry practice in houston" which has approximately 6,620,000 pages indexed.
The number of monthly searches done on any keyword of interest can be discovered at http://www.google.com/sktool. If you go to that page and type in "dentist" you will see that 11,100,000 searches were done on that phrase last month. The number of searches done last month for "dentist office" was 74,000 searches, and the "dentist office houston" phrase had ZERO searches last month.
Obviously a website ranking in the first page of Google for "dentist" is going to have much larger traffic volume, but a dentist ranking for that root keyword globally may have patient inquiries from Boston even though his practice is in Houston. A dentist may be able to claim "First Place Ranking" for "dentist office houston" but if no one is searching by that keyword phrase it has no value.
Determining the most likely keyword phrases where you can economically afford to compete to drive the highest traffic is the real goal of Keyword Selection.
Hope that helps lay a foundation, and please understand that there is TONS more detail below all this, so the importance of having a Strategic Keyword Selection process cannot be over emphasized.
To learn more about Keyword Selection, please visit http://www.worldclassid.com/
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Are You Confused About Social Networking?
Are You Confused About Social Networking?
WorldClass Brand Management | 1155 Camino del Mar #521 | Del Mar | CA | 92014
Social Media Marketing Health - You need solid social networking software to promote your business
Are You Confused About Social Networking?
WorldClass Brand Management | 1155 Camino del Mar #521 | Del Mar | CA | 92014
Are You Confused About Social Networking?
Are You Confused About Social Networking?
WorldClass Brand Management | 1155 Camino del Mar #521 | Del Mar | CA | 92014