Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• The history, core concepts, and practical applications of artificial intelligence, moving beyond the fantastical often associated with the field
• Collective Intelligence: aggregating knowledge shared by numerous virtual agents
• Genetic algorithms: evolving a population of virtual agents through selection
• Definition of a standard learning machine
• 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, and Random Tree
• Machine learning versus Deep Learning: identifying problems where traditional Machine Learning remains the 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 neural network: classical architecture, activation functions
• Weighting of previous activations and network depth
• Learning in neural networks: 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.). The curse of dimensionality.
• Distinction between multi-feature data and signals. Selecting appropriate cost functions based on the data.
• Approximating functions with neural networks: presentations and examples
• Approximating distributions with neural networks: presentations and examples
• Data Augmentation: techniques for balancing a dataset
• Generalizing the results of a neural network.
• Initializing and regularizing a neural network: L1 / L2 regularization, Batch Normalization
• Optimization and convergence algorithms

Standard ML / DL Tools

A concise overview will cover the advantages, disadvantages, ecosystem positioning, and usage of these tools.

• 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).

• Overview of CNNs: fundamental principles and applications
• Basic CNN operations: convolutional layers, kernel usage,
• Padding & stride, feature map generation, and pooling layers. Extensions to 1D, 2D, and 3D.
• Overview of CNN architectures that have advanced the state of the art in classification
• Images: LeNet, VGG Networks, Network in Network, Inception, Resnet. Innovations introduced by each architecture and their broader applications (e.g., 1x1 convolution or residual connections)
• Utilizing attention models.
• Application to common classification tasks (text or image)
• CNNs for generation: super-resolution and pixel-to-pixel segmentation. Overview of
• Key strategies for enhancing 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, and the unfolded version.
• Evolution towards Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).
• Discussion of different states and the advancements brought by these architectures
• Convergence and vanishing gradient problems
• Classical architectures: time series prediction, classification ...
• RNN Encoder-Decoder architecture. Utilization of attention models.
• NLP applications: word / character encoding and translation.
• Video Applications: predicting the next generated image in a video sequence.

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

• Overview of generative models and their link to CNNs
• Auto-encoders: dimensionality reduction and limited generation
• Variational Auto-encoders: generative models and distribution approximation. Definition and use of latent space. The reparameterization trick. Observed applications and limitations
• Generative Adversarial Networks: Fundamentals.
• Dual Network Architecture (Generator and discriminator) with alternate learning and available cost functions.
• GAN convergence and associated challenges.
• Improved 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 in a defined environment
• Based on a state and possible actions
• Using a neural network to approximate the state function
• Deep Q Learning: experience replay and application to video game control.
• Policy learning optimization. On-policy & off-policy. Actor-critic architecture. A3C.
• Applications: controlling a single video game or a digital system.

Part 2 – Theano for Deep Learning

Theano Basics

• Introduction
• Installation and Configuration

TheanoFunctions

• 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

• 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

• Preparing the Data
• Downloading
• Inputs and Placeholders
• Building the Graphs
  • Inference
  • Loss
  • Training
• Training the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluating the Model
  • Building 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
• Enhancing neural network learning techniques

Convolutional Neural Networks

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

Brief introductions to the following modules (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