Understanding Deep Neural Networks Training Course

Part 1 – Concepts of Deep Learning and DNNs

Introduction to AI, Machine Learning & Deep Learning

• History, core concepts, and standard applications of artificial intelligence, moving beyond the field's common misconceptions
• Collective Intelligence: aggregating knowledge shared among multiple virtual agents
• Genetic algorithms: evolving a population of virtual agents through selection processes
• ‌ Definition of traditional Learning Machines
• Types of tasks: supervised learning, unsupervised learning, and reinforcement learning
• Types of actions: 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 Machine Learning remains the current state of the art (e.g., Random Forests & XGBoost)

Foundational Concepts of Neural Networks (Application: multi-layer perceptron)

• Review of mathematical fundamentals
• Defining a neural network: classical architecture, activation functions, and
• Weighting of preceding activations and network depth
• ‌ Defining neural network learning: cost functions, back-propagation, Stochastic gradient descent, and maximum likelihood
• Modeling neural networks: adapting input and output data to the problem type (regression, classification, etc.) and addressing 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: techniques for balancing datasets
• Generalizing the results of neural networks
• Initialization and regularization of neural networks: L1 / L2 regularization and Batch Normalization
• Optimization and convergence algorithms

Standard ML / DL Tools

A brief overview of their advantages, disadvantages, ecosystem position, and typical use cases is included.

• Data management tools: Apache Spark and Apache Hadoop
• Machine Learning: 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 applications
• Basic CNN operations: convolutional layers and kernel usage
• Padding & stride, feature map generation, and pooling layers. Extensions to 1D, 2D, and 3D
• Overview of various CNN architectures that have established the state of the art in classification
• Image processing: LeNet, VGG Networks, Network in Network, Inception, and Resnet. Analysis of innovations introduced by each architecture and their broader applications (e.g., 1x1 convolution or residual connections)
• Utilizing 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 activation, back propagation through time, and the Unfolded version
• Progression towards Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory)
• Overview of various states and the advancements provided by these architectures
• Convergence and vanishing gradient issues
• Classical architectures: time-series prediction, classification, and more
• RNN Encoder-Decoder architecture. Application 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 modeling and distribution approximation. Defining and utilizing 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 associated challenges
• Improved convergence techniques: Wasserstein GAN, Began, and Earth Moving Distance
• Applications in image or photo generation, text generation, and super-resolution

Deep Reinforcement Learning.

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

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

Model Testing

Part 3 – DNNs 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

• Data Preparation
• Downloading
• Inputs and Placeholders
• Graph Construction
  • Inference
  • Loss
  • Training
• Model Training
  • The Graph
  • The Session
  • Training Loop
• Model Evaluation
  • 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 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
• Enhancing neural network learning methods

Convolutional Neural Networks

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

Basic Introductions to the following modules (Brief introduction 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