Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction AI, Machine Learning & Deep Learning

• The history, basic concepts, and common applications of artificial intelligence, distinguishing reality from fantasy
• Collective Intelligence: aggregating knowledge shared by multiple virtual agents
• Genetic algorithms: evolving a population of virtual agents through selection
• Definition of a standard 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 Forest
• Machine learning vs. Deep Learning: identifying problems where Machine Learning remains the 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 neural network: classical architecture, activation functions
• Weighting of previous activations and network depth
• Defining the learning process: 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
• Distinguishing between multi-feature data and signals, and selecting appropriate cost functions
• Approximating functions using neural networks: theory and examples
• Approximating distributions using neural networks: theory and examples
• Data Augmentation: strategies for balancing a dataset
• Generalization of neural network results
• Initialization and regularization: L1/L2 regularization and Batch Normalization
• Optimization and convergence algorithms

Standard ML/DL Tools

This section provides an overview of key tools, highlighting their advantages, disadvantages, ecosystem position, and usage.

• Data management tools: Apache Spark, Apache Hadoop
• 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)

• Introduction to CNNs: fundamental principles and applications
• Core operations: convolutional layers, kernel usage
• Padding, stride, feature map generation, and pooling layers; extensions to 1D, 2D, and 3D
• Overview of CNN architectures that have set the state of the art in classification
• Key architectures: LeNet, VGG, Network in Network, Inception, ResNet; analyzing innovations and broader applications (e.g., 1x1 convolutions or residual connections)
• Utilizing attention models
• Applying CNNs to common classification tasks (text or image)
• CNNs for generation: super-resolution and pixel-to-pixel segmentation
• Primary strategies for enhancing feature maps in image generation

Recurrent Neural Networks (RNN)

• Introduction to RNNs: fundamental principles and applications
• Core operations: hidden activation, back propagation through time, and the unfolded version
• Evolution towards Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory)
• Analysis of different states and architectural evolutions
• Addressing convergence and vanishing gradient issues
• Classic 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 frame in a video sequence

Generative models: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN)

• Overview of generative models and their relationship with CNNs
• Auto-encoders: dimensionality reduction and limited generation
• Variational Auto-encoders: generative modeling, distribution approximation, latent space definition, reparameterization trick, and observed applications/limits
• Generative Adversarial Networks: fundamentals
• Dual Network Architecture (Generator and Discriminator) with alternating learning and available cost functions
• GAN convergence and common difficulties
• Improved convergence techniques: Wasserstein GAN, Began, and Earth Moving Distance
• Applications: image/photograph generation, text generation, and super-resolution

Deep Reinforcement Learning

• Introduction to reinforcement learning: controlling an agent in a defined environment
• Understanding states and possible actions
• Using neural networks to approximate state functions
• Deep Q Learning: experience replay and application to video game control
• Policy optimization: on-policy and off-policy methods, 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 to train models at scale
• Visualizing and evaluating models with TensorBoard

TensorFlow Mechanics

• Preparing the Data
• Data Download
• Inputs and Placeholders
• Building the Graph
  • Inference
  • Loss
  • Training
• Training the Model
  • The Graph
  • The Session
  • Training Loop
• Evaluating the Model
  • Building the Eval Graph
  • Eval 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

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

Basic introductions to the following modules (brief overviews 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