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Deep Learning and WebGL
WebGL Meetup 03/27/2018
GTC SJ Convention Center
Oswald Campesato
oswald@perceptrons.io
ocampesato@yahoo.com
Overview
intro to AI/ML/DL
linear regression
activation/cost functions
gradient descent
back propagation
hyper-parameters
what are CNNs
JS/WebGL and DL
The Data/AI Landscape
Gartner 2017: Deep Learning (YES!)
The Official Start of AI (1956)
Neural Network with 3 Hidden Layers
AI/ML/DL: How They Differ
Traditional AI (20th century):
based on collections of rules
Led to expert systems in the 1980s
The era of LISP and Prolog
AI/ML/DL: How They Differ
Machine Learning:
Started in the 1950s (approximate)
Alan Turing and “learning machines”
Data-driven (not rule-based)
Many types of algorithms
Involves optimization
AI/ML/DL: How They Differ
Deep Learning:
Started in the 1950s (approximate)
The “perceptron” (basis of NNs)
Data-driven (not rule-based)
large (even massive) data sets
Involves neural networks (CNNs: ~1970s)
Lots of heuristics
Heavily based on empirical results
AI/ML/DL: Commonality
All of them involve a model
A model represents a system
Goal: a good predictive model
The model is based on:
Many rules (for AI)
data and algorithms (for ML)
large sets of data (for DL)
A Basic Model in Machine Learning
Let’s perform the following steps:
1) Start with a simple model (2 variables)
2) Generalize that model (n variables)
3) See how it might apply to a NN
Linear Regression
One of the simplest models in ML
Fits a line (y = m*x + b) to data in 2D
Finds best line by minimizing MSE:
m = average of x values (“mean”)
b also has a closed form solution
Linear Regression in 2D: example
Sample Cost Function #1 (MSE)
Linear Regression: example #1
One feature (independent variable):
X = number of square feet
Predicted value (dependent variable):
Y = cost of a house
A very “coarse grained” model
We can devise a much better model
Linear Regression: example #2
Multiple features:
X1 = # of square feet
X2 = # of bedrooms
X3 = # of bathrooms (dependency?)
X4 = age of house
X5 = cost of nearby houses
X6 = corner lot (or not): Boolean
a much better model (6 features)
Linear Multivariate Analysis
General form of multivariate equation:
Y = w1*x1 + w2*x2 + . . . + wn*xn + b
w1, w2, . . . , wn are numeric values
x1, x2, . . . , xn are variables (features)
Properties of variables:
Can be independent (Naïve Bayes)
weak/strong dependencies can exist
Neural Network with 3 Hidden Layers
Neural Networks: equations
Node “values” in first hidden layer:
N1 = w11*x1+w21*x2+…+wn1*xn
N2 = w12*x1+w22*x2+…+wn2*xn
N3 = w13*x1+w23*x2+…+wn3*xn
. . .
Nn = w1n*x1+w2n*x2+…+wnn*xn
Similar equations for other pairs of layers
Neural Networks: Matrices
From inputs to first hidden layer:
Y1 = W1*X + B1 (X/Y1/B1: vectors; W1: matrix)
From first to second hidden layers:
Y2 = W2*X + B2 (X/Y2/B2: vectors; W2: matrix)
From second to third hidden layers:
Y3 = W3*X + B3 (X/Y3/B3: vectors; W3: matrix)
 Apply an “activation function” to y values
Neural Networks (general)
Multiple hidden layers:
Layer composition is your decision
Activation functions: sigmoid, tanh, RELU
https://en.wikipedia.org/wiki/Activation_function
Back propagation (1980s)
https://en.wikipedia.org/wiki/Backpropagation
=> Initial weights: small random numbers
Euler’s Function
The sigmoid Activation Function
The tanh Activation Function
The ReLU Activation Function
What’s the “Best” Activation Function?
Initially: sigmoid was popular
Then: tanh became popular
Now: RELU is preferred (better results)
Softmax: for FC (fully connected) layers
NB: sigmoid and tanh are used in LSTMs
Sample Cost Function #1 (MSE)
Sample Cost Function #2
Sample Cost Function #3
CNNs versus RNNs
CNNs (Convolutional NNs):
Good for image processing
2000: CNNs processed 10-20% of all checks
=> Approximately 60% of all NNs
RNNs (Recurrent NNs):
Good for NLP and audio
Convolutional Neural Networks
CNNs: Convolution Calculations
https://docs.gimp.org/en/plug-in-
convmatrix.html
CNNs: Convolution Matrices (examples)
Sharpen:
Blur:
CNNs: Convolution Matrices (examples)
Edge detect:
Emboss:
How do Filters Work?
 compute “inner product” with an image
The “padding” parameter
Filter size: 3x3, 5x5, or 1x1 are common
Stride values: 1 or 2 (possibly greater)
“parallelize” multiple filters then merge
CNNs: Max Pooling Example
CNN in Python/Keras (fragment)
 from keras.models import Sequential
 from keras.layers.core import Dense, Dropout, Flatten, Activation
 from keras.layers.convolutional import Conv2D, MaxPooling2D
 from keras.optimizers import Adadelta
 input_shape = (3, 32, 32)
 nb_classes = 10
 model = Sequential()
 model.add(Conv2D(32, (3, 3), padding='same’,
input_shape=input_shape))
 model.add(Activation('relu'))
 model.add(Conv2D(32, (3, 3)))
 model.add(Activation('relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
What is TensorFlow?
An open source framework for ML and DL
A “computation” graph
Created by Google (released 11/2015)
Evolved from Google Brain
Linux and Mac OS X support (VM for Windows)
TF home page: https://www.tensorflow.org/
What is TensorFlow?
Support for Python, Java, C++
Desktop, server, mobile device (TensorFlow Lite)
CPU/GPU/TPU support
Visualization via TensorBoard
Can be embedded in Python scripts
Installation: pip install tensorflow
TensorFlow cluster:
https://www.tensorflow.org/deploy/distributed
TensorFlow Use Cases (Generic)
Image recognition
Computer vision
Voice/sound recognition
Time series analysis
Language detection
Language translation
Text-based processing
Handwriting Recognition
What is TensorFlow?
Graph: graph of operations (DAG)
Sessions: contains Graph(s)
lazy execution (default)
operations in parallel (default)
Nodes: operators/variables/constants
Edges: tensors
=> graphs are split into subgraphs and
executed in parallel (or multiple CPUs)
TensorFlow Graph Execution
Execute statements in a tf.Session() object
Invoke the “run” method of that object
“eager” execution is now possible
Not part of the mainline yet
Installation: pip install tf-nightly
What is a Tensor?
TF tensors are n-dimensional arrays
TF tensors are very similar to numpy ndarrays
scalar number: a zeroth-order tensor
vector: a first-order tensor
matrix: a second-order tensor
3-dimensional array: a 3rd order tensor
https://dzone.com/articles/tensorflow-simplified-
examples
TensorFlow “primitive types”
 tf.constant: initialized immediately
 tf.placeholder (a function):
+ initial value is not required
+ assigned value via feed_dict at run time
+ are not modified during training
 tf.Variable (a class):
+ initial value is required
+ updated during training
+ in-memory buffer (saved/restored from disk)
+ can be shared between works (distributed env)
TensorFlow: constants (immutable)
 import tensorflow as tf # tf-const.py
 aconst = tf.constant(3.0)
 print(aconst)
# output: Tensor("Const:0", shape=(), dtype=float32)
 sess = tf.Session()
 print(sess.run(aconst))
# output: 3.0
 sess.close()
 # => there's a better way…
TensorFlow: constants
import tensorflow as tf # tf-const2.py
aconst = tf.constant(3.0)
print(aconst)
Automatically close “sess”
with tf.Session() as sess:
 print(sess.run(aconst))
TensorFlow Arithmetic
import tensorflow as tf # basic1.py
a = tf.add(4, 2)
b = tf.subtract(8, 6)
c = tf.multiply(a, 3)
d = tf.div(a, 6)
with tf.Session() as sess:
print(sess.run(a)) # 6
print(sess.run(b)) # 2
print(sess.run(c)) # 18
print(sess.run(d)) # 1
TensorFlow Arithmetic Methods
import tensorflow as tf #tf-math-ops.py
PI = 3.141592
sess = tf.Session()
print(sess.run(tf.div(12,8)))
print(sess.run(tf.floordiv(20.0,8.0)))
print(sess.run(tf.sin(PI)))
print(sess.run(tf.cos(PI)))
print(sess.run(tf.div(tf.sin(PI/4.), tf.cos(PI/4.))))
TensorFlow Arithmetic Methods
Output from tf-math-ops.py:
1
2.0
6.27833e-07
-1.0
1.0
TF placeholders and feed_dict
import tensorflow as tf # tf-var-multiply.py
a = tf.placeholder("float")
b = tf.placeholder("float")
c = tf.multiply(a,b)
# initialize a and b:
feed_dict = {a:2, b:3}
# multiply a and b:
with tf.Session() as sess:
print(sess.run(c, feed_dict))
Deep Learning JavaScript Toolkits
Tensorfire ( https://tenso.rs )
deeplearn.js ( https://deeplearnjs.org )
 keras-js ( https://github.com/transcranial/keras-js )
MXNetJS ( https://github.com/dmlc/mxnet.js )
ML.js ( https://github.com/mljs/ml )
Jeeliz ( https://jeeliz.com )
What is TensorFire?
uses WebGL to run NNs in browsers
TensorFire has two parts:
1) a GLSL-based language to write massively parallel
WebGL shaders that operate on 4D tensors
2) a high-level library for importing models trained with
Keras or TensorFlow
What is TensorFire?
imitates the structure of TensorFlow & numpy
uses WebGL JavaScript API for 2D/3D graphics
can also perform computations on the GPU
hence it offers significant performance
What is TensorFire?
it supports low-precision quantized tensors
supports browsers that don't fully support the
OES_texture_float extension
https://petewarden.com/2015/05/23/why-are-eight-
bits-enough-for-deep-neural-networks
What is TensorFire?
works on any GPU (with/without CUDA)
on computers with AMD graphics
the latest 2016 Retina Macbook Pro
https://github.com/tensorfire
What is TensorFire?
the Tensorfire demo app:
1) runs the style-transfer neural network
2) renders in your browser
3) as fast as CPU TensorFlow on a desktop
https://tenso.rs/demos/fast-neural-style/
https://tenso.rs/demos/rock-paper-scissors/
TensorFire Code Sample
Go to ‘demos’ directory
Go to backprop subdirectory
Open backprop.html
https://github.com/tensorfire/tensorfire/blob/master/demos/bac
kprop/backprop.html
What is deeplearn.js?
an open-source library
developed by the Google Brain PAIR team
API closely mirrors TensorFlow eager API
can be used with TypeScript (preferred)
can be used with plain JavaScript
What is deeplearn.js?
train NNs in a browser OR
run pre-trained models (inference mode)
A hardware-accelerated machine intelligence
library for the web
What is deeplearn.js?
install via yarn/npm:
yarn add deeplearn or npm install deeplearn
available as a global variable named dl
Use a version (e.g. 0.5.0) instead of @latest:
<script src="https://cdn.jsdelivr.net/npm/deeplearn@0.5.0">
</script>
OR
<script src="https://unpkg.com/deeplearn@latest"></script>
What is deeplearn.js?
 import * as dl from 'deeplearn';
 const a = dl.tensor1d([1, 2, 3]);
 const b = dl.scalar(2);
 const result = a.add(b);
 //Float(32Array([3,4,5])
 result.data().then(data => console.log(data));
 // Alternatively can use a blocking call:
 //Float(32Array([3,4,5])
 console.log(result.dataSync());
deeplearn.js Code Sample
Go to ‘demos’ directory
Go to mnist_eager subdirectory
Open model.ts
https://github.com/PAIR-
code/deeplearnjs/blob/master/demos/mnist_eager/model.ts
deeplearn.js Useful Links
port TensorFlow models to deeplearn.js:
https://deeplearnjs.org/demos/mnist/mnist.html
API reference:
https://deeplearnjs.org/docs/api/index.html
Other samples:
https://deeplearnjs.org/index.html#demos
Deep Learning, JS, and GPU Support
Matrix comparison of JS toolkits:
 https://towardsdatascience.com/gpu-accelerated-neural-
networks-in-javascript-195d6f8e69ef
About Me: Recent Books
1) HTML5 Canvas and CSS3 Graphics (2013)
2) jQuery, CSS3, and HTML5 for Mobile (2013)
3) HTML5 Pocket Primer (2013)
4) jQuery Pocket Primer (2013)
5) HTML5 Mobile Pocket Primer (2014)
6) D3 Pocket Primer (2015)
7) Python Pocket Primer (2015)
8) SVG Pocket Primer (2016)
9) CSS3 Pocket Primer (2016)
10) Android Pocket Primer (2017)
11) Angular Pocket Primer (2017)
12) Data Cleaning Pocket Primer (2018)
13) RegEx Pocket Primer (2018)
About Me: Training
=> Deep Learning. Keras, and TensorFlow:
http://codeavision.io/training/deep-learning-workshop
=> Instructor at UCSC (May/2018):
Deep Learning with TensorFlow
=> Mobile and TensorFlow Lite (WIP)
=> R and Deep Learning (WIP)
=> Android for Beginners

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Deep Learning in your Browser: powered by WebGL

  • 1. Deep Learning and WebGL WebGL Meetup 03/27/2018 GTC SJ Convention Center Oswald Campesato oswald@perceptrons.io ocampesato@yahoo.com
  • 2. Overview intro to AI/ML/DL linear regression activation/cost functions gradient descent back propagation hyper-parameters what are CNNs JS/WebGL and DL
  • 4. Gartner 2017: Deep Learning (YES!)
  • 5. The Official Start of AI (1956)
  • 6. Neural Network with 3 Hidden Layers
  • 7. AI/ML/DL: How They Differ Traditional AI (20th century): based on collections of rules Led to expert systems in the 1980s The era of LISP and Prolog
  • 8. AI/ML/DL: How They Differ Machine Learning: Started in the 1950s (approximate) Alan Turing and “learning machines” Data-driven (not rule-based) Many types of algorithms Involves optimization
  • 9. AI/ML/DL: How They Differ Deep Learning: Started in the 1950s (approximate) The “perceptron” (basis of NNs) Data-driven (not rule-based) large (even massive) data sets Involves neural networks (CNNs: ~1970s) Lots of heuristics Heavily based on empirical results
  • 10. AI/ML/DL: Commonality All of them involve a model A model represents a system Goal: a good predictive model The model is based on: Many rules (for AI) data and algorithms (for ML) large sets of data (for DL)
  • 11. A Basic Model in Machine Learning Let’s perform the following steps: 1) Start with a simple model (2 variables) 2) Generalize that model (n variables) 3) See how it might apply to a NN
  • 12. Linear Regression One of the simplest models in ML Fits a line (y = m*x + b) to data in 2D Finds best line by minimizing MSE: m = average of x values (“mean”) b also has a closed form solution
  • 13. Linear Regression in 2D: example
  • 15. Linear Regression: example #1 One feature (independent variable): X = number of square feet Predicted value (dependent variable): Y = cost of a house A very “coarse grained” model We can devise a much better model
  • 16. Linear Regression: example #2 Multiple features: X1 = # of square feet X2 = # of bedrooms X3 = # of bathrooms (dependency?) X4 = age of house X5 = cost of nearby houses X6 = corner lot (or not): Boolean a much better model (6 features)
  • 17. Linear Multivariate Analysis General form of multivariate equation: Y = w1*x1 + w2*x2 + . . . + wn*xn + b w1, w2, . . . , wn are numeric values x1, x2, . . . , xn are variables (features) Properties of variables: Can be independent (Naïve Bayes) weak/strong dependencies can exist
  • 18. Neural Network with 3 Hidden Layers
  • 19. Neural Networks: equations Node “values” in first hidden layer: N1 = w11*x1+w21*x2+…+wn1*xn N2 = w12*x1+w22*x2+…+wn2*xn N3 = w13*x1+w23*x2+…+wn3*xn . . . Nn = w1n*x1+w2n*x2+…+wnn*xn Similar equations for other pairs of layers
  • 20. Neural Networks: Matrices From inputs to first hidden layer: Y1 = W1*X + B1 (X/Y1/B1: vectors; W1: matrix) From first to second hidden layers: Y2 = W2*X + B2 (X/Y2/B2: vectors; W2: matrix) From second to third hidden layers: Y3 = W3*X + B3 (X/Y3/B3: vectors; W3: matrix)  Apply an “activation function” to y values
  • 21. Neural Networks (general) Multiple hidden layers: Layer composition is your decision Activation functions: sigmoid, tanh, RELU https://en.wikipedia.org/wiki/Activation_function Back propagation (1980s) https://en.wikipedia.org/wiki/Backpropagation => Initial weights: small random numbers
  • 26. What’s the “Best” Activation Function? Initially: sigmoid was popular Then: tanh became popular Now: RELU is preferred (better results) Softmax: for FC (fully connected) layers NB: sigmoid and tanh are used in LSTMs
  • 30. CNNs versus RNNs CNNs (Convolutional NNs): Good for image processing 2000: CNNs processed 10-20% of all checks => Approximately 60% of all NNs RNNs (Recurrent NNs): Good for NLP and audio
  • 33. CNNs: Convolution Matrices (examples) Sharpen: Blur:
  • 34. CNNs: Convolution Matrices (examples) Edge detect: Emboss:
  • 35. How do Filters Work?  compute “inner product” with an image The “padding” parameter Filter size: 3x3, 5x5, or 1x1 are common Stride values: 1 or 2 (possibly greater) “parallelize” multiple filters then merge
  • 36. CNNs: Max Pooling Example
  • 37. CNN in Python/Keras (fragment)  from keras.models import Sequential  from keras.layers.core import Dense, Dropout, Flatten, Activation  from keras.layers.convolutional import Conv2D, MaxPooling2D  from keras.optimizers import Adadelta  input_shape = (3, 32, 32)  nb_classes = 10  model = Sequential()  model.add(Conv2D(32, (3, 3), padding='same’, input_shape=input_shape))  model.add(Activation('relu'))  model.add(Conv2D(32, (3, 3)))  model.add(Activation('relu'))  model.add(MaxPooling2D(pool_size=(2, 2)))  model.add(Dropout(0.25))
  • 38. What is TensorFlow? An open source framework for ML and DL A “computation” graph Created by Google (released 11/2015) Evolved from Google Brain Linux and Mac OS X support (VM for Windows) TF home page: https://www.tensorflow.org/
  • 39. What is TensorFlow? Support for Python, Java, C++ Desktop, server, mobile device (TensorFlow Lite) CPU/GPU/TPU support Visualization via TensorBoard Can be embedded in Python scripts Installation: pip install tensorflow TensorFlow cluster: https://www.tensorflow.org/deploy/distributed
  • 40. TensorFlow Use Cases (Generic) Image recognition Computer vision Voice/sound recognition Time series analysis Language detection Language translation Text-based processing Handwriting Recognition
  • 41. What is TensorFlow? Graph: graph of operations (DAG) Sessions: contains Graph(s) lazy execution (default) operations in parallel (default) Nodes: operators/variables/constants Edges: tensors => graphs are split into subgraphs and executed in parallel (or multiple CPUs)
  • 42. TensorFlow Graph Execution Execute statements in a tf.Session() object Invoke the “run” method of that object “eager” execution is now possible Not part of the mainline yet Installation: pip install tf-nightly
  • 43. What is a Tensor? TF tensors are n-dimensional arrays TF tensors are very similar to numpy ndarrays scalar number: a zeroth-order tensor vector: a first-order tensor matrix: a second-order tensor 3-dimensional array: a 3rd order tensor https://dzone.com/articles/tensorflow-simplified- examples
  • 44. TensorFlow “primitive types”  tf.constant: initialized immediately  tf.placeholder (a function): + initial value is not required + assigned value via feed_dict at run time + are not modified during training  tf.Variable (a class): + initial value is required + updated during training + in-memory buffer (saved/restored from disk) + can be shared between works (distributed env)
  • 45. TensorFlow: constants (immutable)  import tensorflow as tf # tf-const.py  aconst = tf.constant(3.0)  print(aconst) # output: Tensor("Const:0", shape=(), dtype=float32)  sess = tf.Session()  print(sess.run(aconst)) # output: 3.0  sess.close()  # => there's a better way…
  • 46. TensorFlow: constants import tensorflow as tf # tf-const2.py aconst = tf.constant(3.0) print(aconst) Automatically close “sess” with tf.Session() as sess:  print(sess.run(aconst))
  • 47. TensorFlow Arithmetic import tensorflow as tf # basic1.py a = tf.add(4, 2) b = tf.subtract(8, 6) c = tf.multiply(a, 3) d = tf.div(a, 6) with tf.Session() as sess: print(sess.run(a)) # 6 print(sess.run(b)) # 2 print(sess.run(c)) # 18 print(sess.run(d)) # 1
  • 48. TensorFlow Arithmetic Methods import tensorflow as tf #tf-math-ops.py PI = 3.141592 sess = tf.Session() print(sess.run(tf.div(12,8))) print(sess.run(tf.floordiv(20.0,8.0))) print(sess.run(tf.sin(PI))) print(sess.run(tf.cos(PI))) print(sess.run(tf.div(tf.sin(PI/4.), tf.cos(PI/4.))))
  • 49. TensorFlow Arithmetic Methods Output from tf-math-ops.py: 1 2.0 6.27833e-07 -1.0 1.0
  • 50. TF placeholders and feed_dict import tensorflow as tf # tf-var-multiply.py a = tf.placeholder("float") b = tf.placeholder("float") c = tf.multiply(a,b) # initialize a and b: feed_dict = {a:2, b:3} # multiply a and b: with tf.Session() as sess: print(sess.run(c, feed_dict))
  • 51. Deep Learning JavaScript Toolkits Tensorfire ( https://tenso.rs ) deeplearn.js ( https://deeplearnjs.org )  keras-js ( https://github.com/transcranial/keras-js ) MXNetJS ( https://github.com/dmlc/mxnet.js ) ML.js ( https://github.com/mljs/ml ) Jeeliz ( https://jeeliz.com )
  • 52. What is TensorFire? uses WebGL to run NNs in browsers TensorFire has two parts: 1) a GLSL-based language to write massively parallel WebGL shaders that operate on 4D tensors 2) a high-level library for importing models trained with Keras or TensorFlow
  • 53. What is TensorFire? imitates the structure of TensorFlow & numpy uses WebGL JavaScript API for 2D/3D graphics can also perform computations on the GPU hence it offers significant performance
  • 54. What is TensorFire? it supports low-precision quantized tensors supports browsers that don't fully support the OES_texture_float extension https://petewarden.com/2015/05/23/why-are-eight- bits-enough-for-deep-neural-networks
  • 55. What is TensorFire? works on any GPU (with/without CUDA) on computers with AMD graphics the latest 2016 Retina Macbook Pro https://github.com/tensorfire
  • 56. What is TensorFire? the Tensorfire demo app: 1) runs the style-transfer neural network 2) renders in your browser 3) as fast as CPU TensorFlow on a desktop https://tenso.rs/demos/fast-neural-style/ https://tenso.rs/demos/rock-paper-scissors/
  • 57. TensorFire Code Sample Go to ‘demos’ directory Go to backprop subdirectory Open backprop.html https://github.com/tensorfire/tensorfire/blob/master/demos/bac kprop/backprop.html
  • 58. What is deeplearn.js? an open-source library developed by the Google Brain PAIR team API closely mirrors TensorFlow eager API can be used with TypeScript (preferred) can be used with plain JavaScript
  • 59. What is deeplearn.js? train NNs in a browser OR run pre-trained models (inference mode) A hardware-accelerated machine intelligence library for the web
  • 60. What is deeplearn.js? install via yarn/npm: yarn add deeplearn or npm install deeplearn available as a global variable named dl Use a version (e.g. 0.5.0) instead of @latest: <script src="https://cdn.jsdelivr.net/npm/deeplearn@0.5.0"> </script> OR <script src="https://unpkg.com/deeplearn@latest"></script>
  • 61. What is deeplearn.js?  import * as dl from 'deeplearn';  const a = dl.tensor1d([1, 2, 3]);  const b = dl.scalar(2);  const result = a.add(b);  //Float(32Array([3,4,5])  result.data().then(data => console.log(data));  // Alternatively can use a blocking call:  //Float(32Array([3,4,5])  console.log(result.dataSync());
  • 62. deeplearn.js Code Sample Go to ‘demos’ directory Go to mnist_eager subdirectory Open model.ts https://github.com/PAIR- code/deeplearnjs/blob/master/demos/mnist_eager/model.ts
  • 63. deeplearn.js Useful Links port TensorFlow models to deeplearn.js: https://deeplearnjs.org/demos/mnist/mnist.html API reference: https://deeplearnjs.org/docs/api/index.html Other samples: https://deeplearnjs.org/index.html#demos
  • 64. Deep Learning, JS, and GPU Support Matrix comparison of JS toolkits:  https://towardsdatascience.com/gpu-accelerated-neural- networks-in-javascript-195d6f8e69ef
  • 65. About Me: Recent Books 1) HTML5 Canvas and CSS3 Graphics (2013) 2) jQuery, CSS3, and HTML5 for Mobile (2013) 3) HTML5 Pocket Primer (2013) 4) jQuery Pocket Primer (2013) 5) HTML5 Mobile Pocket Primer (2014) 6) D3 Pocket Primer (2015) 7) Python Pocket Primer (2015) 8) SVG Pocket Primer (2016) 9) CSS3 Pocket Primer (2016) 10) Android Pocket Primer (2017) 11) Angular Pocket Primer (2017) 12) Data Cleaning Pocket Primer (2018) 13) RegEx Pocket Primer (2018)
  • 66. About Me: Training => Deep Learning. Keras, and TensorFlow: http://codeavision.io/training/deep-learning-workshop => Instructor at UCSC (May/2018): Deep Learning with TensorFlow => Mobile and TensorFlow Lite (WIP) => R and Deep Learning (WIP) => Android for Beginners