Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• History, fundamental concepts, and typical applications of artificial intelligence, separating reality from hype
• Collective Intelligence: aggregating knowledge shared across many virtual agents
• Genetic algorithms: evolving populations of virtual agents through selection
• Standard Machine Learning: definitions and scope
• Task types: supervised learning, unsupervised learning, reinforcement learning
• Action types: classification, regression, clustering, density estimation, dimensionality reduction
• Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Trees
• Machine Learning vs. Deep Learning: areas where traditional Machine Learning remains state-of-the-art (e.g., Random Forests & XGBoosts)

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

• Review of mathematical foundations
• Definition of a neuron network: classical architecture, activation functions, and
• Weighting of previous activations, network depth
• Definition of neural network learning: cost functions, back-propagation, Stochastic Gradient Descent, maximum likelihood
• Neural network modeling: mapping input/output data based on problem type (regression, classification, etc.). Addressing the curse of dimensionality
• Distinguishing between multi-feature data and signals. Selecting appropriate cost functions for different data types
• Function approximation using neural networks: presentation and examples
• Distribution approximation using neural networks: presentation and examples
• Data Augmentation: techniques to balance datasets
• Generalization of neural network results
• Initialization and regularization of neural networks: L1/L2 regularization, Batch Normalization
• Optimization and convergence algorithms

Standard ML / DL Tools

A concise overview of tools, including advantages, disadvantages, ecosystem positioning, and 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, pooling layers. Extensions for 1D, 2D, and 3D data
• Overview of CNN architectures that achieved state-of-the-art results in classification
• Image architectures: LeNet, VGG Networks, Network in Network, Inception, ResNet. Presentation of innovations introduced by each architecture and their broader applications (e.g., 1x1 Convolution, residual connections)
• Implementation of attention models
• Application to common classification tasks (text or image)
• CNNs for generation: super-resolution, pixel-to-pixel segmentation. Presentation of
• Key strategies for expanding feature maps in image generation

Recurrent Neural Networks (RNN)

• Introduction to RNNs: fundamental principles and applications
• Core RNN operations: hidden activation, back-propagation through time, unfolded architecture
• Evolution toward Gated Recurrent Units (GRUs) and LSTM (Long Short-Term Memory)
• Overview of different states and architectural evolutions
• Convergence and vanishing gradient challenges
• Classic architectures: time series prediction, classification, etc.
• RNN Encoder-Decoder architecture. Implementation of attention models
• NLP applications: word/character encoding, machine 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 capabilities
• Variational Auto-encoders: generative modeling and distribution approximation for given data. Definition and use of latent space. The reparameterization trick. Applications and observed limitations
• Generative Adversarial Networks: Fundamentals
• Dual network architecture (Generator and Discriminator) with alternating learning and available cost functions
• GAN convergence and associated challenges
• Improved convergence methods: Wasserstein GAN, BEGAN. Earth Mover Distance
• Applications: image or photo generation, text generation, super-resolution

Deep Reinforcement Learning

• Introduction to reinforcement learning: controlling an agent within a defined environment
• Based on state and possible actions
• Using neural networks to approximate state functions
• Deep Q-Learning: experience replay and application to video game control
• Optimization of learning policies. On-policy & off-policy. Actor-Critic architecture. A3C
• Applications: control of single video games or digital systems

Part 2 – Theano for Deep Learning

Theano Basics

• Introduction
• Installation and Configuration

Theano Functions

• Inputs, outputs, updates, givens

Training and Optimization of a Neural Network Using Theano

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

Testing the Model

Part 3 – DNN Using TensorFlow

TensorFlow Basics

• Creation, Initialization, 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

• Data Preparation
• Downloading Data
• Inputs and Placeholders
• Building the Graphs
  • 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 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 the way neural networks learn

Convolutional Neural Networks

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

Basic Introductions to the Following Modules (Brief Introductions Provided Based on 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