Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• History, core concepts, and common applications of artificial intelligence, separating reality from hype.
• Collective Intelligence: aggregating knowledge shared by multiple virtual agents.
• Genetic algorithms: evolving a population of virtual agents through selection.
• Standard Machine Learning: definition.
• 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 Tree.
• Machine Learning vs. Deep Learning: areas where 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.
• Defining network learning: cost functions, back-propagation, Stochastic Gradient Descent, maximum likelihood.
• Neural network modeling: representing input and output data based on the problem type (regression, classification) and addressing the curse of dimensionality.
• Distinguishing between multi-feature data and signals. Selecting cost functions based on data characteristics.
• Function approximation by neural networks: presentation and examples.
• Distribution approximation by neural networks: presentation and examples.
• Data Augmentation: techniques for balancing datasets.
• Generalization of neural network results.
• Initialization and regularization: L1 / L2 regularization, Batch Normalization.
• Optimization and convergence algorithms.

Standard ML / DL Tools

A comprehensive overview including advantages, disadvantages, ecosystem position, and use cases will be provided.

• Data management tools: Apache Spark, Apache Hadoop Tools.
• Machine Learning libraries: Numpy, Scipy, Sci-kit.
• High-level DL frameworks: PyTorch, Keras, Lasagne.
• Low-level DL frameworks: Theano, Torch, Caffe, TensorFlow.

Convolutional Neural Networks (CNN).

• Overview of CNNs: fundamental principles and applications.
• Basic CNN operations: convolutional layers, kernel usage.
• Padding & stride, feature map generation, pooling layers, and 1D, 2D, 3D extensions.
• 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).
• Utilization of attention models.
• Application to common classification tasks (text or image).
• CNNs for generation: super-resolution, pixel-to-pixel segmentation.
• Main strategies for increasing feature maps in image generation.

Recurrent Neural Networks (RNN).

• Overview of RNNs: fundamental principles and applications.
• Basic RNN operations: hidden activations, back propagation through time, unfolded version.
• Evolution towards Gated Recurrent Units (GRUs) and LSTM (Long Short-Term Memory).
• Presentation of different states and architectural evolutions.
• Convergence and vanishing gradient issues.
• Classical architectures: time series prediction, classification, etc.
• RNN Encoder-Decoder architecture. Use of attention models.
• NLP applications: word/character encoding, translation.
• Video applications: predicting the next image in a video sequence.

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

• Overview of generative models and their connection to CNNs.
• Auto-encoder: dimensionality reduction and limited generation.
• Variational Auto-encoder: generative modeling and distribution approximation. Definition and use of latent space. 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 difficulties.
• Improved convergence methods: Wasserstein GAN, Began. Earth Mover Distance.
• Applications for image/photo generation, text generation, and super-resolution.

Deep Reinforcement Learning.

• Overview of reinforcement learning: agent control in a defined environment.
• Defined by state and possible actions.
• Using neural networks to approximate state functions.
• Deep Q Learning: experience replay and application to video game control.
• Learning policy optimization. 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, 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

• Creation, Initializing, Saving, and Restoring TensorFlow variables
• Feeding, Reading and Preloading TensorFlow Data
• Utilizing TensorFlow infrastructure for large-scale model training
• Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics

• Prepare the Data
• Download
• Inputs and Placeholders
• Build the Graphs
  • Inference
  • Loss
  • Training
• Train the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluate the Model
  • Build the Eval Graph
  • Eval 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 be given to the below modules (Brief Introduction to be 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