Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• The history, basic concepts, and common applications of artificial intelligence, distinguishing reality from fantasy in this field
• Collective Intelligence: aggregating knowledge shared by multiple virtual agents
• Genetic algorithms: evolving a population of virtual agents through selection
• Standard Machine Learning: definition
• 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 Tree
• Machine Learning vs Deep Learning: identifying problems where Machine Learning remains the state of the art (e.g., Random Forests & XGBoosts)

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

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

Standard ML / DL Tools

This section provides a brief overview of key tools, outlining their advantages, disadvantages, position in the ecosystem, and practical use cases.

• Data management tools: Apache Spark and Apache Hadoop Tools
• Machine Learning: Numpy, Scipy, and Sci-kit
• High-level DL frameworks: PyTorch, Keras, and Lasagne
• Low-level DL frameworks: Theano, Torch, Caffe, and Tensorflow

Convolutional Neural Networks (CNN)

• Overview of CNNs: fundamental principles and applications
• Basic operations in a CNN: convolutional layers and kernel usage
• Padding & stride, feature map generation, and pooling layers, including 1D, 2D, and 3D extensions
• Survey of CNN architectures that have advanced the state of the art in classification
• Image models: LeNet, VGG Networks, Network in Network, Inception, and Resnet, including the innovations each architecture introduced and their broader applications (e.g., 1x1 convolution or residual connections)
• Implementation of 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)

• Overview of RNNs: fundamental principles and applications
• Basic operations in an RNN: hidden activation, back propagation through time, and unfolded versions
• Evolution towards Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory)
• Analysis of different states and architectural evolutions
• Convergence issues and the vanishing gradient problem
• Classical architectures: temporal series prediction and classification
• RNN Encoder-Decoder architectures and the use of attention models
• NLP applications: word / character encoding and translation
• Video applications: predicting the next image in a video sequence

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

• Overview of generative models and their relationship with CNNs
• Auto-encoders: dimensionality reduction and limited generation
• Variational Auto-encoders: generative models approximating data distributions, definition and use of latent space, reparameterization trick, and observed applications and limitations
• Generative Adversarial Networks: Fundamentals
• Dual Network Architecture (Generator and Discriminator) with alternate learning and available cost functions
• GAN convergence and associated challenges
• Improved convergence techniques: Wasserstein GAN, Began, and Earth Moving Distance
• Applications in image/photograph generation, text generation, and super-resolution

Deep Reinforcement Learning

• Introduction to reinforcement learning: controlling an agent in a defined environment
• State representation and possible actions
• Using neural networks to approximate the state function
• Deep Q Learning: experience replay and application to video game control
• 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

Theano Functions

• Inputs, outputs, updates, and givens

Training and Optimization of a Neural Network using Theano

• Neural Network Modeling
• Logistic Regression
• Hidden Layers
• Training a network
• Computation and Classification
• Optimization
• Log Loss

Testing the Model

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 Data
• Inputs and Placeholders
• Building 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 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 neural network learning processes

Convolutional Neural Networks

• Goals
• Model Architecture
• Principles
• Code Organization
• Launching and Training the Model
• Evaluating a Model

Brief Introductions to the Following Modules (Covered based on time availability):

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 Inception Model Tutorial