Fundamentals of Deep Learning and Generative AI Training

• Introduction to Neural Networks and Deep Learning
  • 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
  • Popular Activation Functions
  • Supervised Model Training
  • Measuring the Error with the Loss (Cost) Function
  • Mini-batches and Epochs
  • Neural Network Training Steps
  • Applying Efficiencies with Autodiff ...
  • Neural Network Libraries and Frameworks
• 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
  • 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
  • Ways to Balance Off the Bias-Variance Ratio
  • Training Error vs Validation Error Diagram
  • Loss (Cost) Functions
  • Loss Function Properties
  • Mean Squared Error (MSE)
  • 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
  • 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
• TensorFlow Introduction
  • What is TensorFlow?
  • The TensorFlow Logo
  • Tensors and Python API
  • Python TensorFlow Interfaces Diagram
  • PyTorch
  • 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
• 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
• 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
• Introduction to RNNs
  • Recurrent Neural Networks (RNNs)
  • How Do RNNs Do It?
  • Feedforward Neural Networks vs RNNs
  • Mathematical Formulations
  • (Simplified) RNN Visual Representations
  • A More Accurate RNN Diagram
  • Sampling the Data
  • Problems with RNNs
  • LSTM and GRU Networks
  • Problems with LSTM and GRU Networks
  • RNNs as a Precursor of Generative AI
• Embeddings
  • Embeddings ...
  • Understanding the Embeddings Visually
  • Dimensionality
  • The Semantic Aspect of Embeddings
  • Word Embeddings in NLP
  • Embeddings in Transformers
  • Cosine Similarity
• 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 ...
  • Transformers
  • The Problem Domain
  • LLMs
  • Multimodality of LLMs
  • Infographic of Multimodality Tasks
  • Generative Foundation Models
  • Inferring Movie from Emoji
  • Fine-Tuning and Transfer Learning
  • 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
  • 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
  • Understanding Model Sizes
  • Physical Model Sizes
  • Quantization
  • Generative Adversarial Networks
  • Generator and Discriminator Networks
  • A High-Level GAN Diagram
  • The Above Generator's Sample Output
  • The Diffusion Models, Names, and “Competition”
  • The Core Diffusion Modeling Idea
  • The Diffusion Process
  • AI Alignment
  • Ethical AI
• 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)
  • Multi-Head Attention
  • The Encoder
  • The Decoder
  • The Head-First Approach ...
  • The Transformer Model Architecture
  • Model Training
  • The Overall Translation (Inference) Process
  • Positional Encoding
  • The Encoding Part (a Big Picture)
  • The Decoder Attention Units
  • Cross-Attention
  • The Decoder Part (a Big Picture)
  • The Attentions Weights Matrix

Lab 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. NLP and NLU with Transformers