Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• The history, basic concepts, and common applications of artificial intelligence, moving beyond the often-fanciful portrayals of the field.
• Collective Intelligence: aggregating knowledge shared by numerous virtual agents.
• Genetic algorithms: evolving a population of virtual agents through selection.
• Definition of standard Machine Learning.
• 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, and the weighting of previous activations.
• Network depth and its implications.
• Defining network learning: cost functions, back-propagation, Stochastic gradient descent, and maximum likelihood.
• Modeling neural networks: structuring input and output data based on the problem type (regression, classification, etc.), and addressing the curse of dimensionality.
• Distinguishing between multi-feature data and signals; selecting an appropriate cost function for the data.
• Function approximation by neural networks: theory and examples.
• Distribution approximation by neural networks: theory and examples.
• Data Augmentation: strategies for balancing datasets.
• Generalizing the results of a neural network.
• Initializing and regularizing neural networks: 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, position in the ecosystem, and typical use cases.

• Data management tools: Apache Spark and Apache Hadoop.
• Machine Learning libraries: Numpy, Scipy, and Sci-kit.
• High-level Deep Learning frameworks: PyTorch, Keras, and Lasagne.
• Low-level Deep Learning 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, and pooling layers. Extensions to 1D, 2D, and 3D.
• Overview of CNN architectures that have driven the state-of-the-art in classification.
• Image processing architectures: LeNet, VGG Networks, Network in Network, Inception, and ResNet. Discussion of the innovations introduced by each and their broader applications (e.g., 1x1 convolutions or residual connections).
• Application of attention models.
• Application to common 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, backpropagation through time, and the unfolded version.
• Evolution into Gated Recurrent Units (GRU) and LSTM (Long Short-Term Memory).
• Overview of different states and the advancements provided by these architectures.
• Convergence issues and the vanishing gradient problem.
• Classical architectures: time series prediction, classification, and more.
• 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)

• Introduction to generative models and their relationship with 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, and observed 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.
• Improved convergence techniques: Wasserstein GAN and BEGAN. Earth Mover’s Distance.
• Applications in image and photograph generation, text generation, and super-resolution.

Deep Reinforcement Learning

• Introduction to reinforcement learning: controlling an agent within a defined environment.
• Managing states and possible actions.
• Utilizing neural networks to approximate the state function.
• Deep Q-Learning: experience replay and its application to controlling video games.
• Policy optimization: on-policy & 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

TheanoFunctions

• Inputs, outputs, updates, and givens.

Training and Optimizing Neural Networks with Theano

• Modeling Neural Networks
• Logistic Regression
• Hidden Layers
• Training a Network
• Computation and Classification
• Optimization
• Log Loss

Model Testing

Part 3 – DNN using TensorFlow

TensorFlow Basics

• Creating, initializing, saving, and restoring TensorFlow variables.
• Feeding, reading, and preloading data in TensorFlow.
• Leveraging TensorFlow infrastructure to train models at scale.
• Visualizing and evaluating models with TensorBoard.

TensorFlow Mechanics

• Data Preparation
• Downloading
• Inputs and Placeholders
• Building the Graph
  • 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 using the perceptron
• Document classification using the perceptron
• Limitations of the perceptron

From Perceptrons 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 processes

Convolutional Neural Networks

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

Brief introductions to the following modules (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