Understanding Deep Neural Networks Training Course

Part 1 – Deep Learning and DNN Concepts

Introduction to AI, Machine Learning & Deep Learning

• Historical context, fundamental principles, and practical applications of artificial intelligence, distinguishing operational realities from speculative expectations.
• Collective Intelligence: methodologies for aggregating knowledge across distributed virtual agents.
• Genetic Algorithms: evolutionary techniques for optimizing populations of virtual agents through selection pressures.
• Learning Machines: formal definitions and operational frameworks.
• Primary learning paradigms: supervised, unsupervised, and reinforcement learning.
• Core task categories: classification, regression, clustering, density estimation, and dimensionality reduction.
• Representative algorithms: Linear Regression, Naive Bayes, and Random Forests.
• Comparative analysis of Machine Learning versus Deep Learning; identifying domains where traditional methods remain state-of-the-art (e.g., Random Forests, XGBoost).

Fundamental Concepts of Neural Networks (Application: Multi-Layer Perceptron)

• Review of essential mathematical foundations.
• Neural Network Architecture: structural components, activation mechanisms, and weight propagation from prior layers.
• Determinants of network depth and layer complexity.
• Training protocols: cost functions, backpropagation, stochastic gradient descent, and maximum likelihood estimation.
• Data modeling strategies for input/output structures based on problem type (e.g., regression, classification). Addressing the curse of dimensionality.
• Distinctions between multi-feature datasets and signal data; selection of appropriate cost functions relative to data characteristics.
• Function approximation via neural networks: theoretical basis and illustrative examples.
• Probability distribution approximation via neural networks: theoretical basis and illustrative examples.
• Data Augmentation techniques for dataset balancing and diversity enhancement.
• Mechanisms for ensuring the generalization capabilities of neural network models.
• Network initialization and regularization strategies, including L1/L2 regularization and Batch Normalization.
• Optimization algorithms and convergence criteria.

Standard ML / DL Tools

A concise overview of available tools, evaluating their advantages, limitations, ecosystem positioning, and suitability for government operations.

• Data management infrastructure: Apache Spark, Apache Hadoop.
• Machine Learning libraries: NumPy, SciPy, Scikit-learn.
• High-level Deep Learning frameworks: PyTorch, Keras, Lasagne.
• Low-level Deep Learning frameworks: Theano, Torch, Caffe, TensorFlow.

Convolutional Neural Networks (CNN).

• Overview of CNNs: core principles and deployment scenarios.
• Operational mechanics: convolutional layers, kernel utilization.
• Parameters: padding, stride, feature map generation, pooling layers. Extensions for 1D, 2D, and 3D data.
• Key CNN architectures achieving state-of-the-art performance in image classification.
• Landmark models: LeNet, VGG, Network in Network, Inception, ResNet. Analysis of architectural innovations (e.g., 1x1 convolutions, residual connections) and their broader implications.
• Implementation of attention mechanisms.
• Case studies: classification tasks involving text or image data.
• Generative applications: super-resolution, pixel-to-pixel segmentation.
• Primary strategies for expanding feature maps in image generation tasks.

Recurrent Neural Networks (RNN).

• Overview of RNNs: core principles and deployment scenarios.
• Operational mechanics: hidden activation states, backpropagation through time, unrolled architectures.
• Architectural advancements: Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) networks.
• State management and evolutionary improvements in RNN-based architectures.
• Challenges regarding convergence and vanishing gradients.
• Standard architectures: time-series prediction, classification tasks.
• Encoder-Decoder frameworks with attention mechanisms.
• Natural Language Processing applications: word and character encoding, machine translation.
• Video analysis applications: prediction of subsequent frames in video sequences.

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

• Overview of generative models and their relationship to CNNs.
• Autoencoders: dimensionality reduction and constrained generation capabilities.
• Variational Autoencoders: generative modeling and distribution approximation. Definition and utilization of latent space. The reparameterization trick. Observed applications and limitations.
• Generative Adversarial Networks: fundamental principles.
• Dual-network architecture (Generator/Discriminator) utilizing adversarial training and available cost functions.
• GAN convergence dynamics and common optimization challenges.
• Enhanced convergence methods: Wasserstein GANs, BeGAN, Earth Mover’s Distance.
• Applications in image/photograph synthesis, text generation, and super-resolution.

Deep Reinforcement Learning.

• Overview of reinforcement learning: agent control within defined environments.
• State-space representation and available action sets.
• Utilization of neural networks to approximate state-value functions.
• Deep Q-Learning: experience replay buffers and application to video game control.
Policy optimization strategies: on-policy vs. off-policy methods. Actor-Critic architectures, A3C algorithm. Applications: control of single-game environments or complex digital systems.

Part 2 – Theano for Deep Learning

Theano Basics

• Introduction to the framework.
• Installation procedures and system configuration.

Theano Functions

• Management of inputs, outputs, updates, and given parameters.

Training and Optimization of a Neural Network using Theano

• Neural network modeling structures.
• Logistic Regression implementation.
• Integration of hidden layers.
• Network training procedures.
• Inference and classification outputs.
• Optimization techniques.
• Log Loss metrics.

Model Testing

Part 3 – DNN using TensorFlow

TensorFlow Basics

• Creation, initialization, persistence, and restoration of TensorFlow variables.
• Data ingestion: feeding, reading, and preloading datasets into TensorFlow.
• Leveraging TensorFlow infrastructure for large-scale model training.
• Model visualization and evaluation using TensorBoard.

TensorFlow Mechanics

• Data preparation workflows.
• Dataset retrieval procedures.
• Inputs and placeholders configuration.
• Graph construction:
  • Inference logic.
  • Loss calculation.
  • Training operations.
• Model training execution:
  • Graph management.
  • Session handling.
  • Training loop implementation.
• Model evaluation:
  • Evaluation graph construction.
  • Output interpretation.

The Perceptron

• Activation functions.
• The perceptron learning algorithm.
• Binary classification implementation.
• Document classification applications.
• Limitations of the perceptron model.

From the Perceptron to Support Vector Machines

• Kernels and the kernel trick methodology.
• Maximum margin classification and support vector theory.

Artificial Neural Networks

• Nonlinear decision boundary formation.
• Feedforward and feedback network architectures.
• Multilayer perceptrons.
• Cost function minimization.
• Forward propagation mechanisms.
• Backpropagation algorithms.
• Enhancements to neural network learning efficiency.

Convolutional Neural Networks

• Project objectives.
• Model architecture design.
• Core operational principles.
• Code structure and organization.
• Model launch and training procedures.
• Model evaluation metrics.

Basic Introductions to be given to the below modules (Brief Introduction to be provided based on time availability):

TensorFlow - Advanced Usage

• Threading and queue management.
• Distributed TensorFlow implementation.
• Documentation standards and model sharing protocols for government use.
• Custom data reader development.
• Manipulation of TensorFlow model file formats.

TensorFlow Serving

• Service introduction.
• Basic serving tutorial.
• Advanced serving tutorial.
• Serving the Inception Model tutorial.