This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks, followed by a TypeScript-based code sample that replicates the Tensorflow playground. Basic knowledge of matrices is helpful for this session.

1. TypeScript and Deep Learning
Shape Security (08/09/2017)
Oswald Campesato
ocampesato@yahoo.com

2. Overview
intro to AI/ML/DL
linear regression
activation functions
cost functions
gradient descent
back propagation
hyper-parameters
what are CNNs

3. The Data/AI Landscape

4. Gartner Hype Curve: Where is Deep Learning?

5. Neural Network with 3 Hidden Layers

6. The Official Start of AI (1956)

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. The Rise of Deep Learning
Massive and inexpensive computing power
Huge volumes of data/Powerful algorithms
The “big bang” in 2009:
”deep-learning neural networks and NVidia GPUs"
Google Brain used NVidia GPUs (2009)

11. 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)

12. 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

13. 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

14. Linear Regression in 2D: example

15. Sample Cost Function #1 (MSE)

16. 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

17. 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)

18. 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

19. Neural Network with 3 Hidden Layers

20. 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

21. 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

22. 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

23. Euler’s Function

24. The sigmoid Activation Function

25. The tanh Activation Function

26. The ReLU Activation Function

27. The softmax Activation Function

28. Activation Functions in Python
import numpy as np
...
# Python sigmoid example:
z = 1/(1 + np.exp(-np.dot(W, x)))
...
# Python tanh example:
z = np.tanh(np.dot(W,x));
# Python ReLU example:
z = np.maximum(0, np.dot(W, x))

29. 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 + tanh are used in LSTMs

30. Even More Activation Functions!
https://stats.stackexchange.com/questions/11525
8/comprehensive-list-of-activation-functions-in-
neural-networks-with-pros-cons
https://medium.com/towards-data-
science/activation-functions-and-its-types-which-
is-better-a9a5310cc8f
https://medium.com/towards-data-science/multi-
layer-neural-networks-with-sigmoid-function-
deep-learning-for-rookies-2-bf464f09eb7f

31. Sample Cost Function #1 (MSE)

32. Sample Cost Function #2

33. Sample Cost Function #3

34. How to Select a Cost Function
1) Depends on the learning type:
=> supervised versus unsupervised/RL
2) Depends on the activation function
3) Other factors
Example:
cross-entropy cost function for supervised
learning on multiclass classification

35. GD versus SGD
SGD (Stochastic Gradient Descent):
+ involves a SUBSET of the dataset
+ aka Minibatch Stochastic Gradient Descent
GD (Gradient Descent):
+ involves the ENTIRE dataset
More details:
http://cs229.stanford.edu/notes/cs229-notes1.pdf

36. Setting up Data & the Model
Normalize the data:
Subtract the ‘mean’ and divide by stddev
[Central Limit Theorem]
Initial weight values for NNs:
Random numbers between -1 and 1
More details:
http://cs231n.github.io/neural-networks-2/#losses

37. What are Hyper Parameters?
higher level concepts about the model such as
complexity, or capacity to learn
Cannot be learned directly from the data in the
standard model training process
must be predefined

38. Hyper Parameters (examples)
# of hidden layers in a neural network
the learning rate (in many models)
the dropout rate
# of leaves or depth of a tree
# of latent factors in a matrix factorization
# of clusters in a k-means clustering

39. Hyper Parameter: dropout rate
"dropout" refers to dropping out units (both hidden
and visible) in a neural network
a regularization technique for reducing overfitting in
neural networks
prevents complex co-adaptations on training data
a very efficient way of performing model averaging
with neural networks

40. How Many Layers in a DNN?
Algorithm #1 (from Geoffrey Hinton):
1) add layers until you start overfitting your
training set
2) now add dropout or some another
regularization method
Algorithm #2 (Yoshua Bengio):
"Add layers until the test error does not improve
anymore.”

41. How Many Hidden Nodes in a DNN?
Based on a relationship between:
# of input and # of output nodes
Amount of training data available
Complexity of the cost function
The training algorithm

42. 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

43. CNNs: Convolution Calculations
https://docs.gimp.org/en/plug-in-
convmatrix.html

44. CNNs: Convolution Matrices (examples)
Sharpen:
Blur:

45. CNNs: Convolution Matrices (examples)
Edge detect:
Emboss:

46. CNNs: Sample Convolutions/Filters

47. CNNs: Max Pooling Example

48. CNNs: convolution-pooling (1)

49. CNNs: convolution and pooling (2)

50. GANs: Generative Adversarial Networks

51. GANs: Generative Adversarial Networks
Make imperceptible changes to images
Can consistently defeat all NNs
Can have extremely high error rate
Some images create optical illusions
https://www.quora.com/What-are-the-pros-and-cons-
of-using-generative-adversarial-networks-a-type-of-
neural-network

52. What is TypeScript?
A superset of JavaScript (ES6): 10/01/2012
A compiled language (tsc compiler)
strong typing and also type inferencing
Type checking during compile time
“minimal” extra compile time overhead
“.ts” files are transpiled into “.js” files (via tsc)
“lib.d.ts” contains TypeScript definitions

53. What is TypeScript?
Optional type-checking system
Interfaces, classes, and constructors
Open source project (github)
Used very heavily in Angular
Supported by ReactJS and VueJS (some?)
=> see demo

54. Activations in TypeScript (nn.ts)
export class Activations {
 public static TANH: ActivationFunction = {
 output: x => (Math as any).tanh(x),
 der: x => {
 let output = Activations.TANH.output(x);
 return 1 - output * output;
 }
 };
 public static RELU: ActivationFunction = {
 output: x => Math.max(0, x),
 der: x => x <= 0 ? 0 : 1
 };

55. Activations in TypeScript (nn.ts)
 public static SIGMOID: ActivationFunction = {
 output: x => 1 / (1 + Math.exp(-x)),
 der: x => {
 let output = Activations.SIGMOID.output(x);
 return output * (1 - output);
 }
 };
 public static LINEAR: ActivationFunction = {
 output: x => x,
 der: x => 1
 };
}

56. Deep Learning Playground
TF playground home page:
http://playground.tensorflow.org
Demo #1:
https://github.com/tadashi-aikawa/typescript-
playground
Converts playground to TypeScript

57. Deep Learning and Art
“Convolutional Blending” images:
=> 19-layer Convolutional Neural Network
www.deepart.io
Bots created their own language:
https://www.recode.net/2017/3/23/14962182/ai-
learning-language-open-ai-research
https://www.fastcodesign.com/90124942/this-
google-engineer-taught-an-algorithm-to-make-
train-footage-and-its-hypnotic

58. What Do I Learn Next?
 PGMs (Probabilistic Graphical Models)
 MC (Markov Chains)
 MCMC (Markov Chains Monte Carlo)
 HMMs (Hidden Markov Models)
 RL (Reinforcement Learning)
 Hopfield Nets
 Neural Turing Machines
 Autoencoders
 Hypernetworks
 Pixel Recurrent Neural Networks
 Bayesian Neural Networks
 SVMs

59. About Me
I provide training for the following:
=> Deep Learning/TensorFlow/Keras
=> Android
=> Angular 4

60. Recent/Upcoming 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)