Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• The history, core concepts, and standard applications of artificial intelligence, distinguishing reality from common misconceptions.
• Collective Intelligence: aggregating knowledge shared among numerous virtual agents.
• Genetic algorithms: evolving a population of virtual agents through selection processes.
• Definition of a standard Learning Machine.
• Task types: supervised learning, unsupervised learning, and reinforcement learning.
• Action types: classification, regression, clustering, density estimation, and dimensionality reduction.
• Examples of Machine Learning algorithms: Linear regression, Naive Bayes, and Random Trees.
• Machine Learning vs. Deep Learning: identifying problems where traditional Machine Learning (e.g., Random Forests & XGBoosts) remains the state of the art.

Basic Concepts of Neural Networks (Application: Multi-layer Perceptron)

• Review of mathematical foundations.
• Definition of a neural network: classical architecture, activation functions,
• Weighting of previous activations and network depth.
• Defining network learning: cost functions, back-propagation, Stochastic gradient descent, and maximum likelihood.
• Modeling neural networks: structuring input and output data based on problem type (regression, classification, etc.) and addressing the curse of dimensionality.
• Distinguishing between multi-feature data and signals; selecting appropriate cost functions based on data characteristics.
• Function approximation by neural networks: overview and examples.
• Distribution approximation by neural networks: overview and examples.
• Data Augmentation: techniques for balancing datasets.
• Generalization of neural network results.
• Initialization and regularization of neural networks: L1 / L2 regularization and Batch Normalization.
• Optimization and convergence algorithms.

Standard ML / DL Tools

This section provides an overview of key tools, highlighting their advantages, disadvantages, ecosystem positioning, and typical use cases.

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

Convolutional Neural Networks (CNN).

• Introduction to CNNs: fundamental principles and applications.
• Basic CNN operations: convolutional layers and kernel usage.
• Padding & stride, feature map generation, and pooling layers, including 1D, 2D, and 3D extensions.
• Overview of CNN architectures that have advanced the state of the art in classification.
• Image architectures: LeNet, VGG Networks, Network in Network, Inception, and ResNet, detailing the innovations and broader applications (e.g., 1x1 convolutions or residual connections) of each.
• Incorporating attention models.
• Application to common classification tasks (text or image).
• CNNs for generation: super-resolution and pixel-to-pixel segmentation.
• Key strategies for enhancing feature maps in image generation.

Recurrent Neural Networks (RNN).

• Introduction to RNNs: fundamental principles and applications.
• Basic RNN operations: hidden activations, back propagation through time, and the unfolded version.
• Evolution toward Gated Recurrent Units (GRUs) and LSTM (Long Short-Term Memory).
• Overview of the different states and improvements introduced by these architectures.
• Addressing convergence and vanishing gradient issues.
• Classical architectures: temporal series prediction, classification, and more.
• RNN Encoder-Decoder architecture and the use of attention models.
• NLP applications: word/character encoding and translation.
• Video applications: predicting the next frame in a video sequence.

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

• Introduction to generative models and their relationship with CNNs.
• Auto-encoders: dimensionality reduction and limited generation capabilities.
• Variational Auto-encoders: generative modeling and distribution approximation, including latent space definition, the reparameterization trick, and observed limitations.
• Generative Adversarial Networks: fundamentals.
• Dual Network Architecture (Generator and Discriminator) involving alternating learning and various cost functions.
• GAN convergence and associated challenges.
• Improved convergence methods: Wasserstein GAN, Began, and Earth Mover’s Distance.
• Applications in image and photo generation, text generation, and super-resolution.

Deep Reinforcement Learning.

• Introduction to reinforcement learning: controlling an agent within a defined environment.
• Based on states and possible actions.
• Using neural networks to approximate state functions.
• Deep Q-Learning: experience replay and application to video game control.
• Learning policy optimization: on-policy and off-policy methods, Actor-Critic architecture, and A3C.
• Applications: controlling single video games or digital systems.

Part 2 – Theano for Deep Learning

Theano Basics

• Introduction
• Installation and Configuration

TheanoFunctions

• Inputs, outputs, updates, and givens

Training and Optimizing Neural Networks with Theano

• Neural Network Modeling
• Logistic Regression
• Hidden Layers
• Training the Network
• Computing and Classification
• Optimization
• Log Loss

Model Testing

Part 3 – DNN using TensorFlow

TensorFlow Basics

• Creating, initializing, saving, and restoring TensorFlow variables.
• Feeding, reading, and preloading TensorFlow data.
• Utilizing TensorFlow infrastructure for large-scale model training.
• Visualizing and evaluating models with TensorBoard.

TensorFlow Mechanics

• Data Preparation
• Downloading
• Inputs and Placeholders
• Building the Graphs
  • Inference
  • Loss
  • Training
• Training the Model
  • The Graph
  • The Session
  • Training Loop
• Evaluating the Model
  • Building the Evaluation Graph
  • Evaluation Output

The Perceptron

• Activation functions
• The perceptron learning algorithm
• Binary classification using the perceptron
• Document classification using the perceptron
• Limitations of the perceptron

From Perceptrons 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 neural network learning techniques

Convolutional Neural Networks

• Objectives
• Model Architecture
• Principles
• Code Organization
• Launching and Training the Model
• Evaluating the Model

Brief introductions to the following modules (content coverage depends on available time):

TensorFlow - Advanced Usage

• Threading and Queues
• Distributed TensorFlow
• Writing documentation and sharing models
• Customizing Data Readers
• Manipulating TensorFlow Model Files

TensorFlow Serving

• Introduction
• Basic Serving Tutorial
• Advanced Serving Tutorial
• Serving the Inception Model Tutorial