Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• History, basic concepts, and common applications of artificial intelligence, separating fact from fiction in the field
• Collective Intelligence: aggregating knowledge shared by numerous virtual agents
• Genetic algorithms: evolving a population of virtual agents through selection
• Definition of conventional machine learning models
• Types of tasks: supervised, unsupervised, and reinforcement learning
• Types of actions: 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 scenarios where traditional ML remains state-of-the-art (e.g., Random Forests & XGBoost)

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

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

Standard ML / DL Tools

A concise presentation covering advantages, disadvantages, ecosystem positioning, and use cases for various tools.

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

Convolutional Neural Networks (CNN).

• Presentation of 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 achieved state-of-the-art results in classification
• Key architectures: LeNet, VGG Networks, Network in Network, Inception, ResNet. Presentation of innovations introduced by each and their broader applications (1x1 Convolution, residual connections)
• Application of attention models
• Application to common classification tasks (text or image)
• CNNs for generation: super-resolution, pixel-to-pixel segmentation
• Main strategies for increasing feature maps in image generation

Recurrent Neural Networks (RNN).

• Presentation 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
• Addressing convergence and vanishing gradient problems
• Classical architectures: time series prediction, classification, etc.
• RNN Encoder-Decoder architecture and use of attention models
• NLP applications: word / character encoding, translation
• Video applications: predicting the next image in a video sequence

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

• Presentation of generative models and their relationship with CNNs
• Auto-encoder: dimensionality reduction and limited generation capabilities
• Variational Auto-encoder: generative modeling and distribution approximation for given data. Definition and use of latent space. 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 encountered difficulties
• Improved convergence methods: Wasserstein GAN, BegAN. Earth Mover's Distance
• Applications for generating images, photographs, text, and super-resolution

Deep Reinforcement Learning.

• Presentation of reinforcement learning: controlling an agent within a defined environment
• Utilizing states and possible actions
• Using a neural network to approximate the state function
• 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
• Using TensorFlow infrastructure to train models at scale
• 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
  • Train 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 the learning process for neural networks

Convolutional Neural Networks

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

Basic Introductions to the following modules (Brief Introduction to be 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