Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• History, core concepts, and common applications of artificial intelligence, distinguishing reality from popular fantasies
• Collective Intelligence: aggregating knowledge shared among multiple virtual agents
• Genetic algorithms: evolving populations of virtual agents through selection processes
• Standard Learning Machine: definition and scope
• 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 traditional machine learning remains the state of the art (e.g., Random Forests & XGBoosts)

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

• Recap of mathematical foundations.
• Definition of a neuron network: classical architecture, activation mechanisms
• Weighting of prior activations and network depth
• Definition of network learning: cost functions, back-propagation, stochastic gradient descent, and maximum likelihood
• Modeling neural networks: adapting input and output data models to problem types (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 neuron networks: theoretical presentation and practical examples
• Distribution approximation by neuron networks: theoretical presentation and practical examples
• Data Augmentation: strategies for balancing datasets
• Generalization of neuron network results
• Initialization and regularization of neural networks: L1 / L2 regularization, and Batch Normalization
• Optimization and convergence algorithms

Standard ML / DL Tools

A brief overview is provided, highlighting advantages, disadvantages, ecosystem positioning, and use cases.

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

Convolutional Neural Networks (CNN).

• Introduction to CNNs: fundamental principles and applications
• Basic CNN operations: convolutional layers, kernel usage
• Padding & stride, feature map generation, pooling layers, and 1D, 2D, and 3D extensions
• Overview of CNN architectures that have driven state-of-the-art classification performance
• Image architectures: LeNet, VGG Networks, Network in Network, Inception, ResNet; detailing innovations and broader applications (e.g., 1x1 convolution or residual connections)
• Utilization 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).

• Introduction to RNNs: fundamental principles and applications.
• Basic RNN operations: hidden activation, back propagation through time, and unfolded versions.
• Evolution towards Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).
• Overview of architectural states and advancements introduced by these models
• Convergence and vanishing gradient problems
• 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 generated image 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 constrained generation
• Variational Auto-encoders: generative modeling and distribution approximation; definition and use of latent space, reparameterization trick, and observed applications and limitations
• Generative Adversarial Networks: Core fundamentals.
• Dual Network Architecture (Generator and discriminator) featuring alternate learning and available cost functions.
• GAN convergence and associated challenges.
• Improved convergence techniques: Wasserstein GAN, Began, and Earth Moving Distance.
• Applications in image and photograph generation, text generation, and super-resolution.

Deep Reinforcement Learning.

• Introduction to reinforcement learning: controlling an agent within a defined environment
• Based on state 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 & off-policy approaches, 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
• Computing 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
• Leveraging TensorFlow infrastructure for large-scale model training
• Visualizing and evaluating models using TensorBoard

TensorFlow Mechanics

• Data Preparation
• Downloading data
• Inputs and Placeholders
• Building the Graphs
  • Inference
  • Loss
  • Training
• Training the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluating the Model
  • Building the Eval Graph
  • Eval 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
• Enhancing neural network learning strategies

Convolutional Neural Networks

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

Brief Introductions to the following modules (coverage dependent 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