Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• History, basic concepts, and common applications of artificial intelligence, moving beyond the myths surrounding this field
• Collective Intelligence: aggregating knowledge shared by many virtual agents
• Genetic algorithms: evolving a population of virtual agents through selection
• Machine Learning: definition
• Task types: supervised learning, unsupervised learning, reinforcement learning
• Action types: classification, regression, clustering, density estimation, dimensionality reduction
• Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Trees
• Machine Learning vs. Deep Learning: problems where Machine Learning remains the state of the art (e.g., Random Forests & XGBoost)

Basic Concepts of a Neural Network (Application: Multi-Layer Perceptron)

• Review of mathematical foundations
• Definition of a neural network: classical architecture, activation functions, and
• Weighting of previous activations, network depth
• Definition of neural network learning: cost functions, back-propagation, Stochastic Gradient Descent, maximum likelihood
• Modeling a neural network: input and output data modeling based on problem type (regression, classification, etc.). The curse of dimensionality.
• Distinction between multi-feature data and signals. Choosing a cost function based on data type
• Function approximation by a neural network: presentation and examples
• Distribution approximation by a neural network: presentation and examples
• Data Augmentation: techniques to balance datasets
• Generalization of neural network results
• Initialization and regularization of neural networks: L1/L2 regularization, Batch Normalization
• Optimization and convergence algorithms

Standard ML / DL Tools

A simple presentation outlining advantages, disadvantages, ecosystem position, and usage is planned.

• Data management tools: Apache Spark, Apache Hadoop tools
• Machine Learning: Numpy, Scipy, Scikit-learn
• High-level DL frameworks: PyTorch, Keras, Lasagne
• Low-level DL frameworks: Theano, Torch, Caffe, TensorFlow

Convolutional Neural Networks (CNN).

• Presentation of CNNs: fundamental principles and applications
• Basic operation of a CNN: convolutional layer, kernel usage
• Padding & stride, feature map generation, pooling layers. 1D, 2D, and 3D extensions.
• Presentation of various CNN architectures that achieved state-of-the-art results in classification
• Images: LeNet, VGG Networks, Network in Network, Inception, ResNet. Presentation of innovations introduced by each architecture and their broader applications (e.g., 1x1 Convolution or residual connections)
• Use of attention models
• Application to common classification cases (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 RNNs: hidden activation, back-propagation through time, unrolled version.
• Evolution towards Gated Recurrent Units (GRUs) and LSTM (Long Short-Term Memory).
• Presentation of different states and architectural advancements
• Convergence and vanishing gradient problems
• Classical architectures: Time series prediction, classification, etc.
• RNN Encoder-Decoder architecture. Use of an attention model.
• NLP applications: word/character encoding, translation.
• Video Applications: predicting the next generated image in a video sequence.

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

• Presentation of generative models and their link with CNNs
• Auto-encoder: dimensionality reduction and limited generation
• Variational Auto-encoder: generative model and approximation of a given distribution. Definition and use of latent space. Reparameterization trick. Applications and observed limitations
• Generative Adversarial Networks: Fundamentals.
• Dual Network Architecture (Generator and discriminator) with alternating learning and available cost functions.
• GAN convergence and encountered difficulties.
• Improved convergence: Wasserstein GAN, BEGAN. Earth Mover's Distance.
• Applications for image or photo generation, text generation, super-resolution.

Deep Reinforcement Learning.

• Presentation of reinforcement learning: controlling an agent in a defined environment
• Based on state and possible actions
• Use of a neural network to approximate the state function
• Deep Q-Learning: experience replay and application to video game control.
• Optimization of learning policy. On-policy && off-policy. Actor-Critic architecture. A3C.
• Applications: controlling a single video game or 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, Initialization, 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
  • Training Loop
• Evaluate the Model
  • Build the Evaluation Graph
  • Evaluation 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 provided for the following modules (Brief Introduction 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