2024-02-04T05:22:16.180Z
nova-2
2-general-nova
2024-01-09.29447
30089e05-99d1-4376-b32e-c263170674af
6422164d-13c0-4c6f-bd39-d0fd6d16f1f7
6eb1726c8a27adbf80e4cfd846e2b9dd2d462dd06fb0bda466fe89183c0471bd
67875a7f-c9c4-48a0-aa55-5bdb8a91c34a
deprecated
TensorFlow, an open source machine learning framework famous for powering deep neural networks with high level code.
It was developed by the Google Brain team and first released in 2015.
It's most commonly used with Python, but can run-in other languages like JavaScript, c plus plus and Java.
At its core.
It's just a library for programming with linear algebra and statistics.
As you know, the word tensor describes a multi linear relationship between sets of algebraic objects within a vector released, a k a a multidimensional array.
What makes it special is its collection of APIs for data processing, visualization, model evaluation, and Deployment that make deep learning accessible to the average developer.
It's extremely portable and is able to run on tiny mobile CPUs or microcontrollers with TensorFlow Lite can run-in the browser with TensorFlow JS while the core library can scale up to multiple GPUs or run on tensor processing units, chips engineered specifically to run TensorFlow at a massive scale.
It's used in medicine for object detection and MRI images, by Twitter to sort your timeline by tweet relevance, by to recommend music by PayPal for fraud detection, in addition to many other applications like self driving cars, natural language processing, and so on.
To build your own neural network right now, create a Python file and install TensorFlow.
Next, we'll need some data like fashion MNIST, which we can automatically import.
The goal is to train a model can predict the clothing type of each image.
TensorFlow has a subclassing API for expert users, but also integrates with the beginner friendly Keras library, which has a sequential API that can easily build neural networks layer by layer.
We start with a flattened layer that takes the 28 by 28 pixel image as an input and converts it into a 1 dimensional array.
This input layer is then fed into a dense layer with a 128 really connected neurons or nodes.
You can think of each node like its own linear regression.
As each data point flows through it, it'll try to guess the output and and gradually update a mapping of weights to determine the importance of a given variable.
In this case, it uses a rectified linear activation function that will output the input if a certain threshold is met.
Otherwise, it will just output 0.
And the behavior of this layer can be customized by tuning its hyperparameters.
Finally, we have our output layer, which is also dense is limited to 10 nodes, which corresponds to the total number of clothing types in the dataset.
Now we can compile the model and tell it to a certain loss function like sparse categorical cross entropy.
As we train the model for multiple epics, its accuracy should really improved.
The end result is a model that makes a prediction with the likelihood that an image is a certain type of clothing.
Congratulations.
You just built a neural network.
This has been TensorFlow in 100 seconds.
Hit the like button if you wanna see more short videos like this.
Thanks for watching and I will see you in the next one.

Speaker 0: TensorFlow, an open source machine learning framework famous for powering deep neural networks with high level code. It was developed by the Google Brain team and first released in 2015. It's most commonly used with Python, but can run-in other languages like JavaScript, c plus plus and Java. At its core. It's just a library for programming with linear algebra and statistics.

As you know, the word tensor describes a multi linear relationship between sets of algebraic objects within a vector released, a k a a multidimensional array. What makes it special is its collection of APIs for data processing, visualization, model evaluation, and Deployment that make deep learning accessible to the average developer. It's extremely portable and is able to run on tiny mobile CPUs or microcontrollers with TensorFlow Lite can run-in the browser with TensorFlow JS while the core library can scale up to multiple GPUs or run on tensor processing units, chips engineered specifically to run TensorFlow at a massive scale. It's used in medicine for object detection and MRI images, by Twitter to sort your timeline by tweet relevance, by to recommend music by PayPal for fraud detection, in addition to many other applications like self driving cars, natural language processing, and so on. To build your own neural network right now, create a Python file and install TensorFlow.

Next, we'll need some data like fashion MNIST, which we can automatically import. The goal is to train a model can predict the clothing type of each image. TensorFlow has a subclassing API for expert users, but also integrates with the beginner friendly Keras library, which has a sequential API that can easily build neural networks layer by layer. We start with a flattened layer that takes the 28 by 28 pixel image as an input and converts it into a 1 dimensional array. This input layer is then fed into a dense layer with a 128 really connected neurons or nodes.

You can think of each node like its own linear regression. As each data point flows through it, it'll try to guess the output and and gradually update a mapping of weights to determine the importance of a given variable. In this case, it uses a rectified linear activation function that will output the input if a certain threshold is met. Otherwise, it will just output 0. And the behavior of this layer can be customized by tuning its hyperparameters.

Finally, we have our output layer, which is also dense is limited to 10 nodes, which corresponds to the total number of clothing types in the dataset. Now we can compile the model and tell it to a certain loss function like sparse categorical cross entropy. As we train the model for multiple epics, its accuracy should really improved. The end result is a model that makes a prediction with the likelihood that an image is a certain type of clothing. Congratulations.

You just built a neural network. This has been TensorFlow in 100 seconds. Hit the like button if you wanna see more short videos like this. Thanks for watching and I will see you in the next one.
TensorFlow, an open source machine learning framework famous for powering deep neural networks with high level code. It was developed by the Google Brain team and first released in 2015. It's most commonly used with Python, but can run-in other languages like JavaScript, c plus plus and Java. At its core.
neural networks
deep learning
machine learning
deepgram
It's just a library for programming with linear algebra and statistics.
As you know, the word tensor describes a multi linear relationship between sets of algebraic objects within a vector released, a k a a multidimensional array. What makes it special is its collection of APIs for data processing, visualization, model evaluation, and Deployment that make deep learning accessible to the average developer. It's extremely portable and is able to run on tiny mobile CPUs or microcontrollers with TensorFlow Lite can run-in the browser with TensorFlow JS while the core library can scale up to multiple GPUs or run on tensor processing units, chips engineered specifically to run TensorFlow at a massive scale.
deep learning
neural networks
software development
software
It's used in medicine for object detection and MRI images, by Twitter to sort your timeline by tweet relevance, by to recommend music by PayPal for fraud detection, in addition to many other applications like self driving cars, natural language processing, and so on. To build your own neural network right now, create a Python file and install TensorFlow. Next, we'll need some data like fashion MNIST, which we can automatically import. The goal is to train a model can predict the clothing type of each image. TensorFlow has a subclassing API for expert users, but also integrates with the beginner friendly Keras library, which has a sequential API that can easily build neural networks layer by layer. We start with a flattened layer that takes the 28 by 28 pixel image as an input and converts it into a 1 dimensional array. This input layer is then fed into a dense layer with a 128 really connected neurons or nodes. You can think of each node like its own linear regression. As each data point flows through it, it'll try to guess the output and and gradually update a mapping of weights to determine the importance of a given variable. In this case, it uses a rectified linear activation function that will output the input if a certain threshold is met. Otherwise, it will just output 0. And the behavior of this layer can be customized by tuning its hyperparameters. Finally, we have our output layer, which is also dense is limited to 10 nodes, which corresponds to the total number of clothing types in the dataset. Now we can compile the model and tell it to a certain loss function like sparse categorical cross entropy. As we train the model for multiple epics, its accuracy should really improved. The end result is a model that makes a prediction with the likelihood that an image is a certain type of clothing. Congratulations. You just built a neural network. This has been TensorFlow in 100 seconds. Hit the like button if you wanna see more short videos like this. Thanks for watching and I will see you in the next one.
data
neural networks
modeling
visualization
geometry
machine learning
data analysis
twitter
measurement
youtube
deepgram
graphic design
artificial intelligence
deep learning
TensorFlow, an open source machine learning framework famous for powering deep neural networks with high level code. It was developed by the Google Brain team and first released in 2015. It's most commonly used with Python, but can run-in other languages like JavaScript, c plus plus and Java. At its core. It's just a library for programming with linear algebra and statistics. As you know, the word tensor describes a multi linear relationship between sets of algebraic objects within a vector released, a k a a multidimensional array. What makes it special is its collection of APIs for data processing, visualization, model evaluation, and Deployment that make deep learning accessible to the average developer. It's extremely portable and is able to run on tiny mobile CPUs or microcontrollers with TensorFlow Lite can run-in the browser with TensorFlow JS while the core library can scale up to multiple GPUs or run on tensor processing units, chips engineered specifically to run TensorFlow at a massive scale. It's used in medicine for object detection and MRI images, by Twitter to sort your timeline by tweet relevance, by to recommend music by PayPal for fraud detection, in addition to many other applications like self driving cars, natural language processing, and so on. To build your own neural network right now, create a Python file and install TensorFlow. Next, we'll need some data like fashion MNIST, which we can automatically import. The goal is to train a model can predict the clothing type of each image. TensorFlow has a subclassing API for expert users, but also integrates with the beginner friendly Keras library, which has a sequential API that can easily build neural networks layer by layer. We start with a flattened layer that takes the 28 by 28 pixel image as an input and converts it into a 1 dimensional array. This input layer is then fed into a dense layer with a 128 really connected neurons or nodes. You can think of each node like its own linear regression. As each data point flows through it, it'll try to guess the output and and gradually update a mapping of weights to determine the importance of a given variable. In this case, it uses a rectified linear activation function that will output the input if a certain threshold is met. Otherwise, it will just output 0. And the behavior of this layer can be customized by tuning its hyperparameters. Finally, we have our output layer, which is also dense is limited to 10 nodes, which corresponds to the total number of clothing types in the dataset. Now we can compile the model and tell it to a certain loss function like sparse categorical cross entropy. As we train the model for multiple epics, its accuracy should really improved. The end result is a model that makes a prediction with the likelihood that an image is a certain type of clothing. Congratulations. You just built a neural network. This has been TensorFlow in 100 seconds. Hit the like button if you wanna see more short videos like this. Thanks for watching and I will see you in the next one.
TensorFlow,
tensorflow
an
an
open
open
source
source
machine
machine
learning
learning
framework
framework
famous
famous
for
for
powering
powering
deep
deep
neural
neural
networks
networks
with
with
high
high
level
level
code.
code
It
it
was
was
developed
developed
by
by
the
the
Google
google
Brain
brain
team
team
and
and
first
first
released
released
in
in
2015.
2015
It's
it's
most
most
commonly
commonly
used
used
with
with
Python,
python
but
but
can
can
run-in
run-in
other
other
languages
languages
like
like
JavaScript,
javascript
c
c
plus
plus
plus
plus
and
and
Java.
java
At
at
its
its
core.
core
It's
it's
just
just
a
a
library
library
for
for
programming
programming
with
with
linear
linear
algebra
algebra
and
and
statistics.
statistics
As
as
you
you
know,
know
the
the
word
word
tensor
tensor
describes
describes
a
a
multi
multi
linear
linear
relationship
relationship
between
between
sets
sets
of
of
algebraic
algebraic
objects
objects
within
within
a
a
vector
vector
released,
released
a
a
k
k
a
a
a
a
multidimensional
multidimensional
array.
array
What
what
makes
makes
it
it
special
special
is
is
its
its
collection
collection
of
of
APIs
apis
for
for
data
data
processing,
processing
visualization,
visualization
model
model
evaluation,
evaluation
and
and
Deployment
deployment
that
that
make
make
deep
deep
learning
learning
accessible
accessible
to
to
the
the
average
average
developer.
developer
It's
it's
extremely
extremely
portable
portable
and
and
is
is
able
able
to
to
run
run
on
on
tiny
tiny
mobile
mobile
CPUs
cpus
or
or
microcontrollers
microcontrollers
with
with
TensorFlow
tensorflow
Lite
lite
can
can
run-in
run-in
the
the
browser
browser
with
with
TensorFlow
tensorflow
JS
js
while
while
the
the
core
core
library
library
can
can
scale
scale
up
up
to
to
multiple
multiple
GPUs
gpus
or
or
run
run
on
on
tensor
tensor
processing
processing
units,
units
chips
chips
engineered
engineered
specifically
specifically
to
to
run
run
TensorFlow
tensorflow
at
at
a
a
massive
massive
scale.
scale
It's
it's
used
used
in
in
medicine
medicine
for
for
object
object
detection
detection
and
and
MRI
mri
images,
images
by
by
Twitter
twitter
to
to
sort
sort
your
your
timeline
timeline
by
by
tweet
tweet
relevance,
relevance
by
by
to
to
recommend
recommend
music
music
by
by
PayPal
paypal
for
for
fraud
fraud
detection,
detection
in
in
addition
addition
to
to
many
many
other
other
applications
applications
like
like
self
self
driving
driving
cars,
cars
natural
natural
language
language
processing,
processing
and
and
so
so
on.
on
To
to
build
build
your
your
own
own
neural
neural
network
network
right
right
now,
now
create
create
a
a
Python
python
file
file
and
and
install
install
TensorFlow.
tensorflow
Next,
next
we'll
we'll
need
need
some
some
data
data
like
like
fashion
fashion
MNIST,
mnist
which
which
we
we
can
can
automatically
automatically
import.
import
The
the
goal
goal
is
is
to
to
train
train
a
a
model
model
can
can
predict
predict
the
the
clothing
clothing
type
type
of
of
each
each
image.
image
TensorFlow
tensorflow
has
has
a
a
subclassing
subclassing
API
api
for
for
expert
expert
users,
users
but
but
also
also
integrates
integrates
with
with
the
the
beginner
beginner
friendly
friendly
Keras
keras
library,
library
which
which
has
has
a
a
sequential
sequential
API
api
that
that
can
can
easily
easily
build
build
neural
neural
networks
networks
layer
layer
by
by
layer.
layer
We
we
start
start
with
with
a
a
flattened
flattened
layer
layer
that
that
takes
takes
the
the
28
28
by
by
28
28
pixel
pixel
image
image
as
as
an
an
input
input
and
and
converts
converts
it
it
into
into
a
a
1
1
dimensional
dimensional
array.
array
This
this
input
input
layer
layer
is
is
then
then
fed
fed
into
into
a
a
dense
dense
layer
layer
with
with
a
a
128
128
really
really
connected
connected
neurons
neurons
or
or
nodes.
nodes
You
you
can
can
think
think
of
of
each
each
node
node
like
like
its
its
own
own
linear
linear
regression.
regression
As
as
each
each
data
data
point
point
flows
flows
through
through
it,
it
it'll
it'll
try
try
to
to
guess
guess
the
the
output
output
and
and
and
and
gradually
gradually
update
update
a
a
mapping
mapping
of
of
weights
weights
to
to
determine
determine
the
the
importance
importance
of
of
a
a
given
given
variable.
variable
In
in
this
this
case,
case
it
it
uses
uses
a
a
rectified
rectified
linear
linear
activation
activation
function
function
that
that
will
will
output
output
the
the
input
input
if
if
a
a
certain
certain
threshold
threshold
is
is
met.
met
Otherwise,
otherwise
it
it
will
will
just
just
output
output
0.
0
And
and
the
the
behavior
behavior
of
of
this
this
layer
layer
can
can
be
be
customized
customized
by
by
tuning
tuning
its
its
hyperparameters.
hyperparameters
Finally,
finally
we
we
have
have
our
our
output
output
layer,
layer
which
which
is
is
also
also
dense
dense
is
is
limited
limited
to
to
10
10
nodes,
nodes
which
which
corresponds
corresponds
to
to
the
the
total
total
number
number
of
of
clothing
clothing
types
types
in
in
the
the
dataset.
dataset
Now
now
we
we
can
can
compile
compile
the
the
model
model
and
and
tell
tell
it
it
to
to
a
a
certain
certain
loss
loss
function
function
like
like
sparse
sparse
categorical
categorical
cross
cross
entropy.
entropy
As
as
we
we
train
train
the
the
model
model
for
for
multiple
multiple
epics,
epics
its
its
accuracy
accuracy
should
should
really
really
improved.
improved
The
the
end
end
result
result
is
is
a
a
model
model
that
that
makes
makes
a
a
prediction
prediction
with
with
the
the
likelihood
likelihood
that
that
an
an
image
image
is
is
a
a
certain
certain
type
type
of
of
clothing.
clothing
Congratulations.
congratulations
You
you
just
just
built
built
a
a
neural
neural
network.
network
This
this
has
has
been
been
TensorFlow
tensorflow
in
in
100
100
seconds.
seconds
Hit
hit
the
the
like
like
button
button
if
if
you
you
wanna
wanna
see
see
more
more
short
short
videos
videos
like
like
this.
this
Thanks
thanks
for
for
watching
watching
and
and
I
i
will
will
see
see
you
you
in
in
the
the
next
next
one.
one
success
In this video, the speaker discusses the use of TensorFlow in machine learning, including its use with Python and other language. They also explain how the system works in creating deep neural networks and how it can be used for various applications such as medicine, object detection, and MRI images. The speaker then describes a neural network creation process, including the use of a flattened layer and output layers, and how to use it to predict clothing types. They also mention a YouTube video showing the process.
TensorFlow in 100 Seconds
TensorFlow is a tool for machine learning capable of building deep neural networks with high-level Python code. It provides developer-friendly APIs that help software engineers train, analyze, and deploy ML models. 

#programming  #deeplearning #100secondsofcode 

🔗 Resources

TensorFlow Docs https://www.tensorflow.org/
Fashion MNIST Tutorial https://www.tensorflow.org/tutorials/keras/classification
Neural Networks Overview for Data Scientists https://www.ibm.com/cloud/learn/neural-networks
Machine Learning in 100 Seconds https://youtu.be/PeMlggyqz0Y

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🔖 Topics Covered

- What is TensorFlow? 
- How to build a neural network with TensorFlow
- What is TensorFlow used for?
- Who created TensorFlow?
- How neural networks work
- Easy neural network tutorial
- What is a  mathematical Tensor?