Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• History, core concepts, and standard applications of artificial intelligence, distinguishing between reality and common misconceptions.
• Collective Intelligence: aggregating knowledge shared across multiple virtual agents.
• Genetic algorithms: evolving a population of virtual agents through selection mechanisms.
• Machine Learning fundamentals: key definitions.
• 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 Neural Networks (Application: Multi-layer Perceptron)

• Refresher on mathematical foundations.
• Defining a neural network: 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: modeling input and output data based on the problem type (regression, classification, etc.). 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: strategies for balancing datasets.
• Generalization of neural network results.
• Initialization and regularization: L1 / L2 regularization and Batch Normalization.
• Optimization and convergence algorithms.

Standard ML / DL Tools

A concise overview will be provided, highlighting the advantages, disadvantages, ecosystem positioning, and typical use cases of these tools.

• Data management tools: Apache Spark and Apache Hadoop Tools.
• Machine Learning libraries: 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)

• Overview of CNNs: fundamental principles and key applications.
• Core CNN operations: convolutional layers, kernel usage,
• Padding and stride, feature map generation, and pooling layers. Extensions to 1D, 2D, and 3D.
• Review of various CNN architectures that have defined the state of the art in classification.
• Image processing: 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 common classification cases (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)

• Overview of RNNs: fundamental principles and applications.
• Core RNN operations: hidden activations, backpropagation through time, and the unfolded version.
• Evolution towards Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM).
• Examining different states and the advancements brought by these architectures.
• Convergence challenges and vanishing gradient problems.
• Classical architectures: time series prediction, classification, etc.
• RNN Encoder-Decoder architectures. Implementation of attention models.
• NLP applications: word / character encoding and translation.
• Video applications: predicting the next frame in a video sequence.

Generative Models: Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN)

• Introduction to generative models and their connection to CNNs.
• Autoencoders: dimensionality reduction and limited generation.
• Variational Autoencoders: generative modeling and approximation of data distributions. Defining and using latent space, the reparameterization trick, and reviewing applications and limitations.
• Generative Adversarial Networks: Fundamentals.
• Dual network architecture (Generator and Discriminator) with alternating learning and available cost functions.
• GAN convergence and common difficulties encountered.
• Improved convergence methods: Wasserstein GAN, Began, 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.
• Managing 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 & off-policy approaches, 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

TheanoFunctions

• Handling inputs, outputs, updates, and givens

Training and Optimizing Neural Networks with Theano

• Neural Network Modeling
• Logistic Regression
• Implementing 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
• Downloading datasets
• Inputs and Placeholders
• Building the Graphs
  • 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 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 to improve neural network learning

Convolutional Neural Networks

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

Brief introductions to the following modules (provided subject to 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