Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• A historical overview, core concepts, and common applications of artificial intelligence, distinguishing practical uses from fictional portrayals
• Collective Intelligence: aggregating knowledge shared among numerous virtual agents
• Genetic algorithms: evolving populations of virtual agents through selection processes
• Definition of a standard learning machine
• Task types: supervised learning, unsupervised learning, and reinforcement learning
• Machine Learning algorithms examples: Linear regression, Naive Bayes, Random Tree
• 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
• Weighting of previous activations and network depth
• Defining network learning: cost functions, back-propagation, stochastic gradient descent, and maximum likelihood
• Modeling neural networks: configuring 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 of neural networks: L1 / L2 regularization, and Batch Normalization
• Optimization and convergence algorithms

Standard ML / DL Tools

A concise overview of key tools, covering their advantages, limitations, position within the ecosystem, and typical use cases.

• Data management tools: Apache Spark, Apache Hadoop Tools
• Machine Learning: 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 CNN operations: convolutional layers, kernel usage,
• Padding & stride, feature map generation, and pooling layers. Extensions to 1D, 2D, and 3D.
• Overview of prominent CNN architectures that have advanced the state-of-the-art in classification
• Key image architectures: LeNet, VGG Networks, Network in Network, Inception, ResNet. Discussion of innovations introduced by each and their broader applications (e.g., 1x1 convolutions or residual connections)
• Implementation of attention models.
• Application to common classification tasks (text or image)
• CNNs for generation: super-resolution and pixel-to-pixel segmentation. Overview of
• Primary strategies for enhancing feature maps in image generation.

Recurrent Neural Networks (RNN).

• Introduction to RNNs: fundamental principles and applications.
• Core RNN operations: hidden activations, back propagation through time, and the unfolded representation.
• Evolution towards Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM).
• Overview of different states and architectural evolutions
• Challenges related to convergence and vanishing gradients
• Classical architectures: time series prediction, classification ...
• NLP applications: word / character encoding and translation.
• Video Applications: predicting the next generated frame in a video sequence.

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

• Overview of generational models and their relationship with CNNs
• Auto-encoders: dimensionality reduction and constrained generation
• Variational Auto-encoders: generative models approximating data distributions. Definition and use of latent space. The reparameterization trick. Applications and observed limitations
• Generative Adversarial Networks: Core fundamentals.
• Dual Network Architecture (Generator and discriminator) with alternating learning processes and available cost functions.
• GAN convergence and common difficulties.
• Applications in image/photograph generation, text generation, and super-resolution.

Deep Reinforcement Learning.

• Introduction to reinforcement learning: controlling an agent within a defined environment
• Using neural networks to approximate state functions
• Deep Q Learning: experience replay and application to video game control.
• Applications: control of 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 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
• Visualizing and evaluating models with TensorBoard

TensorFlow Mechanics

• Data Preparation
• Data Download
• Inputs and Placeholders
• Building the Graphs
  • Inference
  • Loss
  • Training
• Model Training
  • The Graph
  • The Session
  • Training Loop
• Model Evaluation
  • 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

Basic Introductions to the following modules (Brief overview provided depending 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