TypeScript and Deep Learning

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

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

Gartner Hype Curve: Where is Deep Learning?

Neural Network with 3 Hidden Layers

The Official Start of AI (1956)

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

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)

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

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 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

The sigmoid Activation Function

The softmax Activation Function

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))

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

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

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

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

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

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

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

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

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.”

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

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

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:

CNNs: Sample Convolutions/Filters

CNNs: convolution and pooling (2)

GANs: Generative Adversarial Networks

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

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

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

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  };

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  }; }

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

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

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

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

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)

TypeScript and Deep Learning

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