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Part 1 – Deep Learning and DNN Concepts

Introduction AI, Machine Learning & Deep Learning

History, basic concepts and usual applications of artificial intelligence far Of the fantasies carried by this domain

Collective Intelligence: aggregating knowledge shared by many virtual agents

Genetic algorithms: to evolve a population of virtual agents by selection

Usual Learning Machine: definition.

Types of tasks: supervised learning, unsupervised learning, reinforcement learning

Types of actions: classification, regression, clustering, density estimation, reduction of dimensionality

Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Tree

Machine learning VS Deep Learning: problems on which Machine Learning remains Today the state of the art (Random Forests & XGBoosts)

Basic Concepts of a Neural Network (Application: multi-layer perceptron)

Reminder of mathematical bases.

Definition of a network of neurons: classical architecture, activation and

Weighting of previous activations, depth of a network

Definition of the learning of a network of neurons: functions of cost, back-propagation, Stochastic gradient descent, maximum likelihood.

Modeling of a neural network: modeling input and output data according to The type of problem (regression, classification ...). Curse of dimensionality.

Distinction between Multi-feature data and signal. Choice of a cost function according to the data.

Approximation of a function by a network of neurons: presentation and examples

Approximation of a distribution by a network of neurons: presentation and examples

Data Augmentation: how to balance a dataset

Generalization of the results of a network of neurons.

Initialization and regularization of a neural network: L1 / L2 regularization, Batch Normalization

Optimization and convergence algorithms

Standard ML / DL Tools

A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.

Data management tools: Apache Spark, Apache Hadoop Tools

Machine Learning: Numpy, Scipy, Sci-kit

DL high level frameworks: PyTorch, Keras, Lasagne

Low level DL frameworks: Theano, Torch, Caffe, Tensorflow

Convolutional Neural Networks (CNN).

Presentation of the CNNs: fundamental principles and applications

Basic operation of a CNN: convolutional layer, use of a kernel,

Padding & stride, feature map generation, pooling layers. Extensions 1D, 2D and 3D.

Presentation of the different CNN architectures that brought the state of the art in classification

Images: LeNet, VGG Networks, Network in Network, Inception, Resnet. Presentation of Innovations brought about by each architecture and their more global applications (Convolution 1x1 or residual connections)

Use of an attention model.

Application to a common classification case (text or image)

CNNs for generation: super-resolution, pixel-to-pixel segmentation. Presentation of

Main strategies for increasing feature maps for image generation.

Recurrent Neural Networks (RNN).

Presentation of RNNs: fundamental principles and applications.

Basic operation of the RNN: hidden activation, back propagation through time, Unfolded version.

Evolutions towards the Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).

Presentation of the different states and the evolutions brought by these architectures

Convergence and vanising gradient problems

Classical architectures: Prediction of a temporal series, classification ...

RNN Encoder Decoder type architecture. Use of an attention model.

NLP applications: word / character encoding, translation.

Video Applications: prediction of the next generated image of a video sequence.

Generational models: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN).

Presentation of the generational models, link with the CNNs

Auto-encoder: reduction of dimensionality and limited generation

Variational Auto-encoder: generational model and approximation of the distribution of a given. Definition and use of latent space. Reparameterization trick. Applications and Limits observed

Generative Adversarial Networks: Fundamentals.

Dual Network Architecture (Generator and discriminator) with alternate learning, cost functions available.

Convergence of a GAN and difficulties encountered.

Improved convergence: Wasserstein GAN, Began. Earth Moving Distance.

Applications for the generation of images or photographs, text generation, super-resolution.

Deep Reinforcement Learning.

Presentation of reinforcement learning: control of an agent in a defined environment

By a state and possible actions

Use of a neural network to approximate the state function

Deep Q Learning: experience replay, and application to the control of a video game.

Optimization of learning policy. On-policy && off-policy. Actor critic architecture. A3C.

Applications: control of a single video game or a digital system.

Part 2 – Theano for Deep Learning

Theano Basics

Introduction

Installation and Configuration

Theano Functions

inputs, outputs, updates, givens

Training and Optimization of a neural network using Theano

Neural Network Modeling

Logistic Regression

Hidden Layers

Training a network

Computing and Classification

Optimization

Log Loss

Testing the model

Part 3 – DNN using Tensorflow

TensorFlow Basics

Creation, Initializing, Saving, and Restoring TensorFlow variables

Feeding, Reading and Preloading TensorFlow Data

How to use TensorFlow infrastructure to train models at scale

Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics

Prepare the Data

Download

Inputs and Placeholders

Build the GraphS

Inference

Loss

Training

Train the Model

The Graph

The Session

Train Loop

Evaluate the Model

Build the Eval Graph

Eval Output

The Perceptron

Activation functions

The perceptron learning algorithm

Binary classification with the perceptron

Document classification with the perceptron

Limitations of the perceptron

From the Perceptron to Support Vector Machines

Kernels and the kernel trick

Maximum margin classification and support vectors

Artificial Neural Networks

Nonlinear decision boundaries

Feedforward and feedback artificial neural networks

Multilayer perceptrons

Minimizing the cost function

Forward propagation

Back propagation

Improving the way neural networks learn

Convolutional Neural Networks

Goals

Model Architecture

Principles

Code Organization

Launching and Training the Model

Evaluating a Model

Basic Introductions to be given to the below modules(Brief Introduction to be provided based on time availability):

Tensorflow - Advanced Usage

Threading and Queues

Distributed TensorFlow

Writing Documentation and Sharing your Model

Customizing Data Readers

Manipulating TensorFlow Model Files

TensorFlow Serving

Introduction

Basic Serving Tutorial

Advanced Serving Tutorial

Serving Inception Model Tutorial