Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• A review of the history, basic concepts, and common applications of artificial intelligence, distinguishing reality from fantasy in the field
• Collective Intelligence: aggregating knowledge shared among numerous virtual agents
• Genetic algorithms: evolving a population of virtual agents through selection processes
• Standard Machine Learning: definition and scope
• 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 & XGBoost)

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

• A refresher on essential mathematical foundations.
• Defining a neural network: classical architecture, activation functions, and
• The weighting of previous activations and network depth
• Defining network learning: cost functions, back-propagation, Stochastic gradient descent, and maximum likelihood.
• Modeling neural networks: adapting input and output data modeling to the problem type (regression, classification, etc.). The Curse of dimensionality.
• Distinguishing between multi-feature data and signals. Selecting an appropriate cost function based on the data.
• Function approximation by neural networks: overview and examples
• Distribution approximation by neural networks: overview and examples
• Data Augmentation: strategies for balancing datasets
• Generalizing the results obtained from neural networks.
• Initialization and regularization of neural networks: L1 / L2 regularization, and Batch Normalization
• Optimization and convergence algorithms

Standard ML / DL Tools

This section offers a concise overview of the advantages, disadvantages, ecosystem positioning, and usage of key tools.

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

• Overview of 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.
• An introduction to various CNN architectures that have established the state of the art in classification
• Image-specific architectures: LeNet, VGG Networks, Network in Network, Inception, and ResNet. A review of the innovations introduced by each and their broader applications (e.g., 1x1 Convolution or residual connections)
• Implementing attention models.
• Application to standard classification cases (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.
• Basic RNN operations: hidden activations, back propagation through time, and the unfolded version.
• Evolution towards Gated Recurrent Units (GRUs) and LSTM (Long Short-Term Memory).
• A look at different states and the advancements brought by these architectures
• Addressing convergence and vanishing gradient problems
• Classical architectures: time series prediction, classification, etc.
• RNN Encoder-Decoder architecture. Use of attention models.
• NLP applications: word / character encoding and translation.
• Video Applications: predicting the next generated 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 capabilities
• Variational Auto-encoders: generative models and distribution approximation. Definition and use of latent space. The Reparameterization trick. 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, Began, and Earth Moving 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
• Characterized by 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 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 Optimizing Neural Networks using Theano

• Neural Network Modeling
• Logistic Regression
• Hidden Layers
• Training a network
• Computing 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 TensorFlow Data
• Leveraging TensorFlow infrastructure for large-scale model training
• 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 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
• Enhancing 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 will be provided, subject to available time:

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