Fundamentals of Deep Learning and Generative AI

Outline

Chapter 1 - Introduction to Neural Networks and Deep Learnin

• What is an Artificial Neural Network?
• Types of Neural Networks
• Machine Learning with Neural Networks
• Deep Learning
• Navigating Neural Networks Layers
• Positional Types of Layers
• The Network and the Model
• Model Properties
• A Bit of Terminology
• Data Pre-processing
• How Does My Network Know Which Problem I Want It to Solve?
• A Neuron
• The Artificial Neuron
• The Perceptron
• The Perceptron Symbol
• A Breakthrough in Neural Networks Design
• Perceptrons and MLPs
• A Basic Neural Network Example
• A Quiz
• Popular Activation Functions
• Quizzes
• A Sample Neural Network Diagram
• Supervised Model Training
• Measuring the Error with the Loss (Cost) Function
• Mini-batches and Epochs
• Neural Network Training Steps (1/2)
• Neural Networks Training Steps (2/2)
• Applying Efficiencies with Autodiff ...
• Neural Network Libraries and Frameworks
• Summary

Chapter 2 - Neural Network Concepts and Terminology

• Why We Need Terminology ...
• Features and Targets
• Observations (Examples)
• Notation for Observations
• Data Structures: Tensors
• Continuous and Categorical Features
• Continuous Features
• Categorical Features
• Feature Types Visually
• Feature Importance
• Supervised and Unsupervised Machine Learning
• Self-Supervised Learning
• Common Distance Metrics
• The Euclidean Distance
• Visualizing Data on the X-Y Plane
• A Quiz
• The Coded Solution to the Quiz
• A Hands-On Exercise
• What is a Model?
• Model Life-Cycles
• Model Parameters and Hyperparameters
• The Train/Validate/Test Machine Learning Triad
• Training/Validation/Test Data Split Ratios
• Data Splitting Considerations
• Cross-Validation Technique
• Test Data Leakage
• Bias-Variance (Underfitting vs Overfitting) Trade-off
• Bias and Variance Visually
• Model Underfitting vs Model Overfitting Visually
• A Quiz
• Ways to Balance Off the Bias-Variance Ratio
• Training Error vs Validation Error Diagram
• Loss (Cost) Functions
• Loss Function Properties
• Mean Squared Error (MSE)
• A Quiz
• Mean Absolute Error (MAE)
• (Categorical) Cross Entropy Loss
• The Cross Entropy Loss Visually
• The Chain Rule in Calculus
• The Chain Rule in Neural Networks
• Gradient Descent in Neural Networks (1/2)
• Gradient Descend Visually
• Gradient Descent in Neural Networks (2/2)
• An Annotated Example of Gradient Calculation
• A Hands-On Exercise
• The softmax Function
• Coding Softmax
• Model Accuracy in Classification Tasks
• Confusion Matrix
• The Binary Classification Confusion Matrix
• Multi-class Classification Confusion Matrix Example
• Feature Engineering
• Data Scaling and Normalization
• The Data Normalization Tooling
• Regularization
• A Hands-On Exercise
• Mathematical Formulations ...
• Dimensionality Reduction
• Online Machine Learning Glossaries
• Summary

Chapter 3 - TensorFlow Introduction

• What is TensorFlow?
• The TensorFlow Logo
• Tensors and Python API
• Python TensorFlow Interfaces Diagram
• GPUs and TPUs
• Google Colab
• Data Tools
• TensorFlow Variants
• TensorFlow Core API
• TensorFlow Lite
• TFX (TensorFlow Extended)
• A TFX Pipeline Example
• XLA Optimization
• TensorFlow Toolkit Stack
• Keras
• TensorBoard
• Summary

Chapter 4 - Introduction to Keras

• What is Keras?
• Keras 3.0
• Core Keras Data Structures
• Layers in Keras
• The Dense Layer
• Defining the Layer Activation Function
• Models in Keras
• Components of a Keras Model
• Creating Neural Networks in Keras
• The Sequential Model
• A Sequential Model Code Example
• The Strengths and Weaknesses of Sequential Models
• The Functional API
• A Functional API Example
• The Strengths and Weaknesses of the Functional API
• Making New Layers and Models via Subclassing
• A Layer Subclassing Example
• A Model Subclassing Example
• The Strengths and Weaknesses of Subclassing
• Summary

Chapter 5 - Introduction to CNNs

• Convolutional Neural Networks (CNNs)
• Kernels and Convolutions
• A Convolution Mathematically
• A Convolution Visually
• A Quiz
• Kernels and Feature Maps
• Feature Maps in CNNs
• CNN Efficiencies
• Feature Maps Visually
• The Stride Hyperparameter
• The CNN Architecture
• The Conv2D Class
• A Quiz
• An Example of Pooling Layer
• Finally, Putting it All Together
• Summary

Chapter 6 - Introduction to RNNs

• Recurrent Neural Networks (RNNs)
• An Example of Predicting the Next Value in a Time Series
• How Do RNNs Do It?
• A Quiz
• Feedforward Neural Networks vs RNNs
• Mathematical Formulations
• (Simplified) RNN Visual Representations
• A Quiz
• A Quiz
• A More Accurate RNN Diagram
• An Example to Build Intuition
• A Quiz
• A Quiz
• Sampling the Data
• Problems with RNNs
• LSTM and GRU Networks
• Problems with LSTM and GRU Networks
• RNNs as a Precursor of Generative AI
• Summary

Chapter 7 - Embeddings

• Embeddings ...
• Understanding the Embeddings Visually
• Dimensionality
• The Semantic Aspect of Embeddings
• Word Embeddings in NLP
• Embeddings in Transformers
• Cosine Similarity
• A Quiz
• An Exercise
• Summary

Chapter 8 - Introduction to Generative AI

• The Age of Digital Assistants ...
• What is Generative AI?
• Applications
• What Are Natural Language Models?
• The Probabilistic Language Model
• Training a Language Model to Predict the Next Word
• Generative AI, the Pre-Cursor Technologies
• RNN Limitations
• Wait, there is More ...
• A Quiz
• A Hands-On Exercise
• Transformers
• The Problem Domain
• LLMs
• Multimodality of LLMs
• Infographic of Multimodality Tasks
• Generative Foundation Models
• Example of an LLM Explaining a Joke
• Example of Cause & Effect Reasoning
• Inferring Movie from Emoji
• Fine-Tuning and Transfer Learning
• A Quiz
• Transfer Learning in Computer Vision
• The Transfer Learning Diagram
• Can I Have My Very Own Model?
• The Age of Digital Assistants ... Transformed ...
• The Training Datasets
• The Training Techniques
• Hugging Face
• The Evolutionary Tree of LLMs
• The LLM Capabilities vs LLM Size (in Parameters)
• Does the Model Size Matter?
• Inference Accuracy vs LLM Size
• OpenAI GPT Models
• ChatGPT
• The Microsoft 365 Copilot Ecosystem
• The LLaMA Family of LLMs
• LLaMA 2
• The AI-Powered Chatbots
• Options for Accessing LLMs
• Cloud Hosting
• Prompt Engineering
• Context Window and Prompts
• Zero- and Few-Shot Prompting
• A Hands-On Exercise
• Understanding Model Sizes
• Physical Model Sizes
• Quantization
• A Quiz
• Generative Adversarial Networks
• Generator and Discriminator Networks
• A High-Level GAN Diagram
• The Above Generator's Sample Output
• A Quiz
• The Diffusion Models, Names, and “Competition”
• The Core Diffusion Modeling Idea
• The Diffusion Process
• Example of a Denoising Process
• AI Alignment
• Ethical AI
• Summary

Chapter 9 - Introduction to Transformers

• What is a Transformer?
• Transformer Use Cases
• Transformers, Encoders, and Decoders
• Recurrent Neural Networks
• Why Transformers?
• The Transformer Evolution Path
• A Short Summary of the Transformer Inner Workings
• N-Grams
• Tokenization
• Two Types of Weights
• (Self-)Attention (1 of 3)
• (Self-)Attention (2 of 3)
• (Self-)Attention (3 of 3)
• Multi-Head Attention
• The Encoder
• The Decoder
• The Head-First Approach ...
• The Transformer Model Architecture
• Model Training
• The Overall Translation (Inference) Process
• An Example of Sentence Syntax Analysis
• Positional Encoding
• The Encoding Part (a Big Picture)
• The Decoder Attention Units
• Cross-Attention
• The Decoder Part (a Big Picture)
• The Attentions Weights Matrix
• Summary

Hands-on Exercises

• Lab 1 - Learning the Colab Jupyter Notebook Environment
• Lab 2 - Neural Network Playground Web App
• Lab 3 - Multi-layer Perceptron Classifier
• Lab 4 - Vectors and Matrix Operations
• Lab 5 - Understanding the Gradient Descent Algorithm
• Lab 6 - Understanding Regularization
• Lab 7 - TensorFlow Basics
• Lab 8 - Using Keras for Image Classification
• Lab 9 - Using CNNs for Image Classification
• Lab 10 - Understanding RNNs
• Lab 11 - Word2vec Pre-Trained Embeddings
• Lab 12 - Hello, Generative AI!
• Lab 13 - Using OpenAI API
• Lab 14 - Colab Code Assistant
• Lab 15 - NLP and NLU with Transformers