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, moving beyond the fantasy often associated with the field
• Collective Intelligence: aggregating knowledge shared among many virtual agents
• Genetic algorithms: evolving populations of virtual agents through selection processes
• Definition of a Learning Machine
• 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 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
• Defining network learning: cost functions, back-propagation, Stochastic gradient descent, and maximum likelihood
• Modeling neural networks: structuring input and output data based on problem type (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 neural networks: theory and examples
• Distribution approximation by neural networks: theory and examples
• Data Augmentation: techniques for balancing datasets
• Generalization of neural network results
• Initialization and regularization of neural networks: L1/L2 regularization and Batch Normalization
• Optimization and convergence algorithms

Standard ML/DL Tools

A concise overview highlighting the advantages, disadvantages, ecosystem positioning, and use cases of key tools.

• Data management tools: Apache Spark, Apache Hadoop
• Machine Learning libraries: NumPy, SciPy, Sci-kit
• High-level Deep Learning frameworks: PyTorch, Keras, Lasagne
• Low-level Deep Learning frameworks: Theano, Torch, Caffe, TensorFlow

Convolutional Neural Networks (CNN)

• Overview of CNNs: fundamental principles and applications
• Core CNN operations: convolutional layers, kernel usage,
• Padding & stride, feature map generation, pooling layers; 1D, 2D, and 3D extensions
• Overview of CNN architectures that have set new benchmarks in classification
• Image classification: LeNet, VGG Networks, Network in Network, Inception, ResNet. Discussion of innovations introduced by each architecture and their broader applications (e.g., 1x1 convolutions or residual connections)
• Implementation of attention models
• Application to standard classification tasks (text or image)
• CNNs for generation: super-resolution and pixel-to-pixel segmentation. Overview of
• Key strategies for enhancing feature maps for image generation

Recurrent Neural Networks (RNN)

• Overview of RNNs: fundamental principles and applications
• Core RNN operations: hidden state activation, back propagation through time, and unfolded versions
• Evolution towards Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM)
• Overview of various states and architectural evolutions
• Addressing convergence and vanishing gradient problems
• Classical architectures: time series prediction, classification
• RNN Encoder-Decoder architectures; utilization 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 relationship with CNNs
• Autoencoders: dimensionality reduction and limited generation capabilities
• Variational Autoencoders: generative modeling 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 training and available cost functions
• GAN convergence and common difficulties
• Improved convergence techniques: Wasserstein GAN, BEGAN, and Earth Mover's Distance
• Applications: image/photograph generation, text generation, and super-resolution

Deep Reinforcement Learning

• Overview of reinforcement learning: agent control within a defined environment
• Interaction based on states and possible actions
• Utilizing neural networks to approximate state-value functions
• Deep Q-Learning: experience replay and application to video game control
• Policy optimization: on-policy vs. off-policy methods; Actor-Critic architecture; 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
• Network Training
• 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 for large-scale model training
• Visualizing and evaluating models with TensorBoard

TensorFlow Mechanics

• Data Preparation
• Data Downloading
• 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
• 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
• Strategies for improving neural network learning

Convolutional Neural Networks

• Objectives
• Model Architecture
• Core Principles
• Code Organization
• Launching and Training the Model
• Model Evaluation

Brief Introductions to the following modules (content provided based on available time):

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