Fundamentals of Deep Learning and Generative AI Training - Phoenix TS (2024)

Phoenix TS

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Course Overview

In this his five day, instructor led Generative AI (GenAI) course in Washington, DC Metro, Tysons Corner, VA, Columbia, MD or Live Online, participants learn the fundamentals of Deep Learning and GenAI, and build the skills to apply these powerful technologies to real-world scenarios. The course introduces attendees to key concepts, tools, and frameworks like TensorFlow and Keras, as well as neural network architectures like CNN, RNN, and Transformers. Students learn about large language models (LLMs), GANs, and diffusion models, and their applications in diverse domains. This course is intended for data practitioners, business analysts, software engineers, and IT architects. At the completion of this course, participants will be able to:

  • Grasp core concepts and terminology for deep learning and generative AI
  • Build neural networks using deep learning frameworks such as TensorFlow and Keras
  • Understand the use cases for different neural network architectures like CNNs and RNNs
  • Investigate the semantic aspect of embeddings and their role in representing data in a lower-dimensional space, including techniques like word embeddings for natural language processing tasks
  • Review the capabilities and applications of large language models (LLMs), including BERT, GPT-3, and LLaMA, investigating their role in natural language processing, creative text generation, and code development
  • Learn about generative AI techniques, including the principles behind generative adversarial networks (GANs)
  • Gain insights into prompt engineering techniques to guide and refine generative AI outputs
  • Explore the relationship between foundation models, fine-tuning, and transfer learning
  • Discuss the ethical considerations and responsible AI practices

Schedule

Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 301-258-8200.

Prerequisites

All learners are required to have:

  • Basic knowledge of Python and familiarity with the NumPy library.

Course Outline

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
  • 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
  • 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

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BONUS! Cyber Phoenix Subscription Included: All Phoenix TS students receive complimentary ninety (90) day access to the Cyber Phoenix learning platform, which hosts hundreds of expert asynchronous training courses in Cybersecurity, IT, Soft Skills, and Management and more!

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