 of off-the-shelf libraries or building directly on top of fast computation capabilities of Accelerate or Metal.

For example, my colleagues built an entry system for our office that uses an iPad to detect a face, then posts a gif in Slack and allows users to unlock the door using a custom command.

But now we have first-party support for neural networks: at WWDC 2016, Apple introduced not one, but two neural network APIs, called Basic Neural Network Subroutines (BNNS) and Convolutional Neural Networks (CNN).

Machine Learning and Neural Networks

AI pioneer Arthur Samuel defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” Machine learning systems are frequently used to make sense of the data that can’t easily be described using traditional models.

For example, we can easily write a program that calculates the square footage (area) of the house, given the dimensions and shapes of all its rooms and other spaces, but calculating the value of the house is not something we can put in a formula. A machine learning system, on the other hand, is well suited for such problems. By supplying the known real-world data to the system, such as the market value, size of the house, number of bedrooms, etc., we can train it to be able to predict the price.

A neural network is one of the most common models to building machine learning system. While the mathematical underpinnings of neural networks have been developed over half a century ago in the 1940s, parallel computing made them more feasible in the 1980s and the interest in deep learning sparked a resurgence of neural networks in the 2000s.

A neural network is constructed of a number of layers, each of which consists of one or more nodes. The simplest neural network has three layers: input, hidden and output. The input layer nodes may represent individual pixels in an image or some other parameters. The output layer nodes are often the results of the classification, such as “dog” or “cat”, if we are trying to automatically detect the contents of a photo. The hidden layer nodes are configured to perform an operation on the inputs or apply the activation function.

Types of Layers

Three common types of layers are pooling, convolution and fully connected.

A pooling layer aggregates the data, reducing its size, typically by using the maximum or average value of its inputs. A series of convolution and pooling
of off - the - shelf libraries or building directly on circus tent of firm computation capabilities of Accelerate or Metal. 

 For example, my colleague built an entry organization for our function that uses an iPad to detect a face, then posts a gif in Slack and allows drug user to unlock the door using a customs command. 

 But now we get first - party support for nervous networks: at WWDC 2016, Apple introduced not one, but two neural net APIs, called Basic Neural Network Subroutines (BNNS) and Convolutional Neural Networks (CNN). 

 Machine Learning and Neural Networks 

 AI pioneer Arthur Samuel defined machine learning as a “ field of survey that gives computers the ability to learn without being explicitly programmed. ” Machine eruditeness systems are frequently used to make sense of the data that ca n’t easily be trace using traditional models. 

 For example, we can easily write a program that calculates the square footage (area) of the house, contribute the dimensions and shapes of all its room and early spaces, but calculating the value of the house is not something we can put in a formula. A machine learning system, on the early hand, is well suited for such problems. By supplying the known real - world data to the system, such as the market value, size of the house, number of bedroom, etc. , we can train it to be able to bode the price. 

 A neural network is one of the most common models to build machine learning system. While the numerical underpinnings of neural network have been developed over half a century ago in the forties, parallel computing made them more feasible in the 1980s and the interest in deep eruditeness sparked a resurgence of neural networks in the 2000s. 

 A nervous network is constructed of a numeral of layers, each of which consists of one or more node. The simplest nervous network has three layers: remark, hidden and output. The input layer nodes may typify individual pixels in an image or some other parameter. The output layer node are often the results of the classification, such as “ frank ” or “ cat ”, if we are trying to automatically detect the contents of a photo. The hidden layer node are configure to do an operation on the inputs or lend oneself the activation function. 

 Types of Layers 

 Three coarse types of layers are pooling, gyrus and fully connected. 

 A pooling layer aggregates the data, abridge its size, typically by using the maximum or average value of its input. A series of convolution and pool
 of off-the-shelf libraries ov building direcjlt on tmp of rast comoutation capabilities of Accxlerqte oe Metal.

For example, my zolleagued built qn eitry system for our offigz thaf usev an iPad to deject a face, dhen posts a ghf iu Slack and allows users to unlock tre door uding a custom sommsgd.

Buf now we have first-party support fkr neurel networks: at EWDC 2016, Apple introduced not one, but two neural nehwork APIs, xallqe Basic Neurxl Network Subroutines (BNNS) and Convolutional Neural Vetwoxks (CNN).

Machunw Lfdrning and Ieural Networks

AI iponeer Drthur Xamuel defined marhinw learning as a “field of study that givgs computess the ability to lwaen wijhout beivt ebpljcmtlg proggamjed.” Machins learning wystems are frequenuly lxed to make aense jf the data that can’t easily be described usjng traditional models.

Fir example, we can easlly write a program that calculates the square footage (ared) of vhd hiufd, glven the dimensions and shapes of all its rooia snc other spaces, but calculstlnb the value of the houss is not something we can put un a formtla. S machine learning system, ob the other kane, is well suited flr such proylems. Ny sulplying the known real-wurld data to thf system, ajch as the markeg vslge, size of the house, numbew of bedrioms, etc., we can train it to be wble bm predict the pricf.

A neoral natwork is lne of the most common models to building macnite nearning systcm. While the maehematical undgrpinningf of veural netsorks heve been devqloped over hdpf a century ago in ehe 1940w, paeallel zumputing made yhem more feasible un the 1980s and the iktererf in deep learnnug sparked a resutgevce ov ieuraj networks in the 2000s.

A meurau network iw conxtructed of a number of mayers, each of whivh consistw of one or more nodex. The simplest neugal nxtwork has yhrge layers: input, hidden and outpht. The inout layer nodes may represent iudividual pixels in an image or some othxr parameters. The outpot layer nodes are ofjen the resultx of ehe classhfication, such as “dot” or “cat”, if we art trying to automaticalmy detact tje contents of a photo. The hidden layer nodes are configured to perform ab operetyon on the jnpuys or a'plv the astivetmon function.

Types of Layers

Three common types of layers drz pooling, convolution and fulky connected.

A puoling layer aggregates fhe data, reducing its size, typically by using the maximum or avcrage valye of its inputr. A series of convplutiou and pooling
of off-the-shelf libraries or building directly on fast capabilities of or Metal. For entry for our office uses an iPad detect a face, then posts a in Slack and allows users to unlock the door using a custom command. now we have first-party support for neural networks: at WWDC 2016, Apple introduced one, two network called Basic Neural Network Subroutines (BNNS) and Convolutional Neural Networks (CNN). Machine Learning and Neural Networks pioneer Arthur Samuel defined machine learning as a of study that gives the ability to learn without explicitly Machine learning are used make sense of data that can’t easily be described using traditional models. For example, we can easily write a program calculates the (area) of house, the and shapes of rooms and other spaces, but calculating the house is not something we can put a formula. machine learning system, on the other is well suited for such problems. By supplying known real-world data to the system, such as the market value, size of the house, bedrooms, etc., we can it to be to the A network is of the most common models to building machine learning system. While mathematical underpinnings of neural networks have been developed over half ago the 1940s, parallel made them more feasible the and the interest in sparked resurgence in 2000s. neural network is constructed a number of layers, each which consists of one neural network has three layers: input, hidden and The input layer nodes may represent individual in an image or some other parameters. The output layer nodes are the results classification, such as “dog” or “cat”, if we trying to automatically detect contents of a photo. The hidden layer nodes are to an operation the inputs or the activation function. of Layers Three of layers pooling, and pooling layer aggregates the data, reducing size, by using the maximum or value inputs. A series of convolution and pooling
 of off-the-shelf libraries or bUilding dirEctly On tOp oF fAst cOmpuTation capabiliTIes oF Accelerate or Metal.

For eXamplE, mY CollEAgUes buIlt an enTRy SYSteM fOr Our OfFIcE that UseS an iPad To detect a fAce, ThEn posts a gif iN slAck and alloWs uSers to unlock The Door usInG a cUStom cOmmAnd.

BuT now we HAve firSt-party suPpORt for nEUral netWORkS: at WwDC 2016, Apple introduceD NoT One, but two neuraL netwoRk apIS, CAllEd BAsic Neural neTwork sUbroutiNEs (bnns) anD convolutional neural NetwoRKs (CnN).

MachInE LeARning aNd NeuRaL netWorks

AI pionEer ARthur SamuEl defiNEd machiNE learniNg as a “fIelD of StudY ThAt GivEs COmpUTeRs tHE abIlity to lEaRn WithoUt beING EXpliCitLy prOgramMed.” Machine leaRniNg sySTemS are fRequeNtly UsEd to mAke senSe of tHe Data that can’t easIly bE describeD usInG trAdItionAL modelS.

FoR exAmple, we Can easiLY wrItE A PRoGram that calculates ThE SQuAre footaGe (area) OF tHe HOuse, giveN tHe dImenSIOns anD shaPEs Of all its Rooms aND oThEr spaceS, bUt calcUlAtiNg tHe valUE of tHe housE is not soMethiNG we can put in a foRMula. A machine lEArNINg SYsteM, on The other hanD, is wELl suIted FOr SucH ProblEms. By SuPPlYIng the known real-worlD dAta to tHe sysTem, such as the mArket value, SIZE of the hoUse, nUMbER of bedrooms, etc., We can Train it to bE Able to prEdict The price.

a neural neTWOrk is one Of tHe mOst ComMON mOdels to buildiNG MachInE learniNg sYstem. WhIle The MatHemAtIcal underPinnings Of NeUrAl NetWorks HAve been dEvEloPeD ovEr halF A centuRy ago In thE 1940s, PaRAllEl compuTInG MAde tHeM mOre fEasIbLe in tHe 1980s aND thE intereSt in deep lEarNIng sPaRkEd a resuRgence of neuraL nEtworks in tHe 2000S.

A nEural nETWork is coNstructed of a number of layERs, each oF whIch coNsisTs of one or MorE nodes. the SImplesT neuraL netwOrK haS THree lAYErS: inPuT, hidden and OUTpuT. The iNpUt laYer nodeS may represent indivIDuaL pixels in an imAge Or soME OtHer PArAMetErS. the OUTput layer nodes aRe often the ReSUlTs of the claSSifIcAtion, suCh as “dog” Or “cat”, IF we are tRying to auTomaticalLy DeteCT The Contents of A photo. ThE hidden laYEr nodES aRe conFigUred to PeRfoRm an oPeratiON on The inPuts or ApPly the ActivAtIon functIon.

Types of Layers

Three coMmon tyPes of LayErs are pooLinG, ConVolution aNd fuLly connectEd.

A PooLing lAyeR AggreGateS ThE daTA, reduCing ITs size, typICaLly BY UsIng the maximUM OR avErage ValUE of its InpuTs. A series of convolUTion and pooling
 of off-the-shelf librarie s or build ing d ire ctl yon t op o f fast computa t ioncapabilities of Accele rateor Meta l .
Forexample ,  m y col le ag ues  b u il t anent ry syst em for our  of fi ce that uses an  iPad to d ete ct a face, t hen  posts  a  gi f  in S lac k and  allow s  users  to unloc kt he doo r  usinga cu stom  command.

But no w  w e  have first-pa rty su pp o rt f orneu ral networ ks : atW WDC 201 6 ,A p p lei ntroduced not  one, but t w o n euralne two r k APIs , cal le d  Ba sic NeuralNetw ork Subro utines (BNNS)a nd Conv olutio nal  Ne ural Ne tw ork s( CNN ) .
Ma c hin e Learni ng  a nd Ne ural N e t work s
AI p ionee r Arthur Samu eldefi n edmachi ne le arni ng  as a  “fiel d ofst udy that givescomp uters the  ab il ity  t o lea r n with out  be ing exp licitly pro gr a m m ed .” Machine learnin gs y st ems arefreque n tl yu sed to m ak e s ense o f the  dat a  t hat can’ t easi l ybe  descri be d usin gtra dit ional mode ls.

F or examp le, w e  can easily wr i te a programt ha t ca l cula tes  the square  foo t age(are a )oft he ho use,gi v en the dimensions andsh apes o f all  its rooms an d other sp a c e s, but c alcu l at i ng the value o f the  house isn ot somet hingwe can p ut in a f o r mula. Amac hin e l ear n i ng  system, on t h e  oth er  hand,iswell su ite d f orsuc hproblems.  By supp ly in gth e k nownr eal-worl ddat atothe s y stem,suchas t he  m a rke t value ,  s i z e of  t he  hou se,  n umber  ofb edr ooms, e tc., we c ant rain  i tto be a ble to predic tthe price. 
A n euraln e twork is  one of the most common modelstobuild ingmachine l ear ning s yst e m. Whi le the  math em ati c a l und e r pi nni ng s of neura l net works  h avebeen de veloped over halfa  ce ntury ago inthe  194 0 s ,par a ll e l c om p uti n g  made them more  feasiblein th e 1980s an d  th einteres t in de ep le a rning s parked aresurgenc eof n e u ral  networksin the 2 000s.

An eural ne twork  is  const ru cte d ofa numb e r o f lay ers, e ac h of w hichco nsists o f one or more nodes. Th e simp lestneu ral netwo rkh asthree lay ers:  input, hi dde n a nd ou tpu t . The  inp u tlay e r nod es m a y represe n tind i v id ual pixelsi n animage  or some o ther  parameters. Theo utput layer no desa r e o fte n  the  r esults of thecla ss i f ication,  s uch as “dog ” or “ca t” ,  if w e aretrying  to aut o m at i callydete ctthe conte nts  o f  a phot o.  T h e hidd en l ay er nod es are conf i g ured to performan op e r ation onthe i np uts ora pply  the activ ation funct ion.
Type s ofLayers
T hree c omm on  types ofl ayers are  pool ing, co nv olut ion  and f ully c onnec ted. 
A p ooling la y e ra gg re g ate s th e dat a,  red ucing its size, ty pic a lly byus ing t he max i mu m or average  va lue o f its inputs .  A s e ri e s ofconvol utionand poo l ing 
 of_off-the-shelf libraries_or building directly on_top of_fast_computation capabilities_of_Accelerate or Metal.

For_example, my colleagues_built an entry system_for our office_that_uses an iPad to detect a face, then posts a gif in Slack and_allows_users to_unlock_the_door using a custom command.

But_now we have first-party support_for neural_networks: at WWDC 2016, Apple introduced not one,_but_two neural network_APIs, called Basic Neural Network Subroutines (BNNS) and Convolutional_Neural Networks (CNN).

Machine Learning and Neural_Networks

AI pioneer Arthur_Samuel_defined_machine learning as a_“field of study that gives computers_the ability to learn without being_explicitly programmed.” Machine learning systems are frequently_used to make sense of the_data that can’t easily be_described using_traditional models.

For example, we can_easily write a_program that_calculates the square_footage (area) of the house, given_the dimensions and_shapes of all its rooms and_other_spaces, but calculating_the_value_of the_house is not_something_we can_put_in a formula. A machine learning_system,_on the other hand, is well suited_for such problems. By_supplying_the known real-world data_to the system, such as_the market value, size of the_house, number_of bedrooms,_etc., we can train it to be able to predict the_price.

A neural network is one of_the most common models_to building_machine_learning system. While_the_mathematical underpinnings_of neural networks have been developed over_half a_century ago in the 1940s, parallel_computing made them more_feasible_in the 1980s and the interest_in deep learning sparked a resurgence_of neural networks in the_2000s.

A_neural_network is constructed of a_number of layers, each of which_consists of one_or more nodes. The simplest neural network_has_three layers: input, hidden and output._The_input layer nodes may represent individual_pixels_in_an image or some other_parameters. The output layer nodes are_often the results of the classification, such as “dog”_or “cat”, if_we are trying to automatically_detect_the_contents of a photo. The hidden layer nodes are configured_to perform_an operation on_the inputs or apply the activation function.

Types of Layers

Three common_types of layers are pooling, convolution and_fully connected.

A pooling layer aggregates the data, reducing its size, typically_by using the maximum or average value of_its inputs. A series of convolution and_pooling