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 typical applications of artificial intelligence, distinguishing reality from the fantasy often associated with the field
• Collective Intelligence: Aggregating knowledge shared by numerous virtual agents
• Genetic algorithms: Evolving populations of virtual agents through selection processes
• Standard Learning Machines: Definitions and core principles
• 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 Forests
• Machine Learning vs. Deep Learning: Identifying problems where traditional machine learning (such as Random Forests and XGBoost) remains the state of the art

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

• A review of essential mathematical foundations
• Defining neural networks: Classical architecture, activation functions
• Weighting of previous activations and the concept of network depth
• Defining network learning: Cost functions, back-propagation, Stochastic Gradient Descent, and maximum likelihood
• Modeling neural networks: Input and output data modeling based on problem type (regression, classification, etc.), and understanding the curse of dimensionality
• Distinguishing between multi-feature data and signals, and 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: Strategies 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

A concise overview covering advantages, disadvantages, ecosystem positioning, and usage is provided.

• Data management tools: Apache Spark and Apache Hadoop tools
• Machine Learning libraries: NumPy, SciPy, and scikit-learn
• 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 CNN operations: Convolutional layers, kernel usage
• Padding, stride, feature map generation, and pooling layers, including 1D, 2D, and 3D extensions
• Introduction to various CNN architectures that have established state-of-the-art performance in classification
• Image processing architectures: LeNet, VGG Networks, Network in Network, Inception, and ResNet, including their innovations and broader applications (e.g., 1x1 convolutions, residual connections)
• Utilizing attention models
• Application to common classification tasks (text or image)
• CNNs for generation: Super-resolution and pixel-to-pixel segmentation, with an overview
• Key strategies for enhancing feature maps for image generation

Recurrent Neural Networks (RNN).

• Overview of RNNs: Fundamental principles and applications
• Basic RNN operations: Hidden activations, back-propagation through time, and unfolded versions
• Evolution towards Gated Recurrent Units (GRUs) and LSTM (Long Short-Term Memory)
• Presentation of different states and architectural advancements
• Convergence and vanishing gradient issues
• Classical architectures: Time series prediction, classification, etc.
• RNN Encoder-Decoder architectures 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).

• Overview of generative models and their connection to CNNs
• Auto-encoders: Dimensionality reduction and limited generation capabilities
• Variational Auto-encoders: Generative models, distribution approximation, definition and use of latent space, the reparameterization trick, and observed applications and limitations
• Generative Adversarial Networks: Fundamental concepts
• Dual Network Architecture (Generator and Discriminator) with alternating learning, and available cost functions
• GAN convergence and common difficulties
• Improved convergence techniques: Wasserstein GAN, BigGAN, and Earth Mover’s Distance
• Applications in image/photograph generation, text generation, and super-resolution

Deep Reinforcement Learning.

• Overview of reinforcement learning: Controlling an agent within a defined environment
• Based on state and possible actions
• Utilizing 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 approaches, Actor-Critic architecture, and A3C
• Applications: Control of a single video game or digital system

Part 2 – Theano for Deep Learning

Theano Basics

• Introduction
• Installation and Configuration

TheanoFunctions

• 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 to train models at scale
• Visualizing and evaluating models using 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 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
• Enhancing neural network learning methods

Convolutional Neural Networks

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

Brief introductions to the following modules (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