Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• The history, fundamental concepts, and common applications of artificial intelligence, moving beyond common misconceptions in the field
• Collective Intelligence: the aggregation of knowledge shared by numerous virtual agents
• Genetic algorithms: evolving a population of virtual agents through selection processes
• Standard Machine Learning: definition 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 Trees
• Machine Learning vs. Deep Learning: identifying problems where traditional machine learning (e.g., Random Forests & XGBoost) remains the state of the art

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
• Definition of network learning: cost functions, back-propagation, Stochastic gradient descent, and maximum likelihood.
• Modeling neural networks: handling input and output data based on problem type (regression, classification, etc.), and addressing the curse of dimensionality.
• Distinguishing between multi-feature data and signals, and selecting appropriate cost functions.
• Function approximation using neural networks: overview and examples
• Distribution approximation using neural networks: overview and examples
• Data Augmentation: techniques for balancing datasets
• Generalizing results from 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 concise overview of key tools, highlighting their advantages, disadvantages, ecosystem positioning, and typical 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).

• 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 various CNN architectures that established the state of the art in classification
• Image models: LeNet, VGG Networks, Network in Network, Inception, and ResNet. Discussion of innovations introduced by each architecture and their broader applications (e.g., 1x1 convolutions or residual connections)
• Utilizing attention models.
• Application to standard 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 unfolded versions.
• Evolution toward Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).
• Overview of different states and advancements brought by these architectures
• Issues with convergence and vanishing gradients
• Classical architectures: time series prediction, classification, and more...
• RNN Encoder-Decoder architecture. 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).

• Introduction to generative models and their connection to CNNs
• Auto-encoders: dimensionality reduction and limited generation
• Variational Auto-encoders: generative models and distribution approximation. Definition and use of latent space, the 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 common challenges.
• Improved convergence techniques: Wasserstein GAN, Began, and Earth Mover’s Distance.
• Applications in image/photo generation, text generation, and super-resolution.

Deep Reinforcement Learning.

• Introduction to reinforcement learning: controlling an agent in 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.
• Optimization of learning policies. On-policy && off-policy methods. Actor-Critic architecture. A3C.
• Applications: controlling a single video game or a digital system.

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

• Creation, initialization, saving, and restoring TensorFlow variables
• Feeding, reading, and preloading TensorFlow data
• Leveraging TensorFlow infrastructure to train models at scale
• Visualizing and evaluating models with TensorBoard

TensorFlow Mechanics

• Data Preparation
• Downloading Data
• 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 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 neural network learning methods

Convolutional Neural Networks

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

Brief introductions to the following modules (content provided 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