Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• History, fundamental concepts, and common applications of artificial intelligence, distinguishing reality from the myths often associated with this field
• Collective Intelligence: aggregating knowledge shared among multiple virtual agents
• Genetic algorithms: evolving populations of virtual agents through selection
• Defining standard learning machines
• 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, Random Trees
• Machine Learning vs. Deep Learning: identifying scenarios where traditional ML (e.g., Random Forests & XGBoosts) remains the state-of-the-art

Basic Concepts of a Neural Network (Application: Multi-layer Perceptron)

• Review of mathematical foundations
• Defining a neuron 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 a neural network: structuring input and output data based on the problem type (regression, classification, etc.) and addressing the curse of dimensionality
• Distinguishing between multi-feature data and signals; selecting appropriate cost functions based on data type
• Approximating functions using neural networks: presentation and examples
• Approximating distributions using neural networks: presentation and examples
• Data Augmentation: techniques for balancing datasets
• Generalizing results from neural networks
• Initialization and regularization techniques: L1 / L2 regularization, Batch Normalization
• Optimization and convergence algorithms

Standard ML / DL Tools

A brief overview covering advantages, disadvantages, ecosystem positioning, and use cases will be provided for the following tools.

• Data management tools: Apache Spark, Apache Hadoop
• 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 and kernel usage
• Padding & stride, feature map generation, and pooling layers. Extensions for 1D, 2D, and 3D data.
• Overview of CNN architectures that achieved state-of-the-art results in classification
• Key image architectures: LeNet, VGG, Network in Network, Inception, ResNet. Overview of innovations introduced by each (e.g., 1x1 Convolution, residual connections) and their broader applications
• Utilization of attention models
• Application to common classification tasks (text or image)
• CNNs for generation: super-resolution, pixel-to-pixel segmentation
• Main strategies for enhancing feature maps in image generation

Recurrent Neural Networks (RNN).

• Overview of RNNs: fundamental principles and applications
• Basic RNN operations: hidden activations, backpropagation through time, and unfolded versions
• Evolution towards Gated Recurrent Units (GRUs) and LSTM (Long Short-Term Memory)
• Overview of different states and architectural improvements
• Addressing convergence and vanishing gradient problems
• Classical architectures: time series prediction, classification, etc.
• Encoder-Decoder architectures. Utilization of attention models
• NLP applications: word / character encoding, 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 connection to CNNs
• Auto-encoders: dimensionality reduction and limited generation capabilities
• Variational Auto-encoders: generative modeling and distribution approximation for given inputs. Definition and usage 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 encountered difficulties
• Improving convergence: Wasserstein GAN, BEGAN. Earth Mover's Distance
• Applications for generating images or photographs, text generation, and super-resolution

Deep Reinforcement Learning.

• Overview of 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
• Optimizing learning policies: On-policy && Off-policy. Actor-Critic architecture. A3C
• Applications: controlling single video games or digital systems

Part 2 – Theano for Deep Learning

Theano Basics

• Introduction
• Installation and Configuration

Theano Functions

• 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 preprocessing TensorFlow data
• Utilizing TensorFlow infrastructure for large-scale model training
• Visualizing and evaluating models with TensorBoard

TensorFlow Mechanics

• Preparing the data
• 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 Outputs

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 neural network learning methods

Convolutional Neural Networks

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

Basic Introductions to be given to the following 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