Understanding Deep Neural Networks Training Course

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

• Introduction

• Installation and Configuration

Theano Functions

• inputs, outputs, updates, givens

• Neural Network Modeling

• Logistic Regression

• Hidden Layers

• Training a network

• Computing and Classification

• Optimization

• Log Loss

Part 3 – DNN using Tensorflow

• 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

• 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

• Activation functions

• The perceptron learning algorithm

• Binary classification with the perceptron

• Document classification with the perceptron

• Limitations of the perceptron

• Kernels and the kernel trick

• Maximum margin classification and support vectors

• 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

• 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