Ultimate Neural Network Programming with Python
Description
Master Neural Networks for Building Modern AI Systems.
DESCRIPTION
This book is a practical guide to the world of Artificial Intelligence (AI), unraveling the math and principles behind applications like Google Maps and Amazon. The book starts with an introduction to Python and AI, demystifies complex AI math, teaches you to implement AI concepts, and explores high-level AI libraries.
Throughout the chapters, readers are engaged with the book through practice exercises and supplementary learnings. The book then gradually moves to Neural Networks with Python before diving into constructing ANN models and real-world AI applications. It accommodates various learning styles, letting readers focus on hands-on implementation or mathematical understanding.
This book isn't just about using AI tools; it's a compass in the world of AI resources, empowering readers to modify and create tools for complex AI systems. It ensures a journey of exploration, experimentation, and proficiency in AI, equipping readers with the skills needed to excel in the AI industry.
TABLE OF CONTENTS
1. Understanding AI History
2. Setting up Python Workflow for AI Development
3. Python Libraries for Data Scientists
4. Foundational Concepts for Effective Neural Network Training
5. Dimensionality Reduction, Unsupervised Learning and Optimizations
6. Building Deep Neural Networks from Scratch
7. Derivatives, Backpropagation, and Optimizers
8. Understanding Convolution and CNN Architectures
9. Understanding Basics of TensorFlow and Keras
10. Building End-to-end Image Segmentation Pipeline
11. Latest Advancements in AI
Index
More details
Content
- Cover Page
- Title Page
- Copyright Page
- Dedication Page
- About the Author
- About the Technical Reviewers
- Welcome note
- Acknowledgements
- Preface
- Errata
- Table of Contents
- 1. Understanding AI History
- Structure
- Evolution of AI
- The early history of AI
- The most crucial development in the History of AI
- AI started evolving into new fields
- AI starts taking its modern form
- Understanding Intelligent Behavior
- AI beats humans at chess
- AI learning reasoning and language
- AI starts playing poker
- Conquering GO and Dota 2
- An experience with ChatGPT
- Difference between Artificial Intelligence, Machine Learning, and Deep Learning
- Formally defining AI terms
- Learning representations from data
- Sub-Fields of AI
- Artificial Intelligence (AI)
- Machine Learning (ML)
- Deep Learning (DL)
- Early Models of Neuron-Inspired Networks
- Understanding biological neurons
- McCulloch-Pitts model of a neuron
- Multilayer Perceptron (MLP)
- Conclusion
- 2. Setting up Python Workflow for AI Development
- Structure
- Setting up Python Environment
- Installing Python
- Getting Anaconda for Data Science Environment Setup
- Setting up a Virtual Environment
- Installing packages
- Setting up VS Code
- Installing Git
- Setting up GitHub with VS Code
- Concepts of OOPS
- Encapsulation
- Accessing Variables
- Inheritance
- Conclusion
- 3. Python Libraries for Data Scientists
- Structure
- Web Scraping
- Regex
- Multi-Threading and Multi-Processing
- Multi-Threading
- Multi-Processing
- Pandas Basics
- Conclusion
- 4. Foundational Concepts for Effective Neural Network Training
- Structure
- Activation Functions
- RBF, Universal Approximators, and Curse of Dimensionality
- Radial Bias Function
- Neural Networks are universal approximators
- The curse of dimensionality
- Overfitting, Bias-Variance, and Generalization
- Overfitting problem
- Regularization and effective parameters
- Dropout
- Early stopping and validation set
- Bias-Variance trade-off
- Generalization
- Conclusion
- 5. Dimensionality Reduction, Unsupervised Learning and Optimizations
- Structure
- Dimensionality reduction
- Principal component analysis (PCA)
- T-SNE
- Non-linear PCA
- Unsupervised learning
- Clustering
- Semi-supervised learning
- Generalizing active learning to multi-class
- Self-supervised learning
- Version space
- Understanding optimization through SVM
- Conclusion
- 6. Building Deep Neural Networks from Scratch
- Structure
- Coding neurons
- A single neuron
- Layer of neurons
- Understanding lists, arrays, tensors, and their operations
- Dot product and vector addition
- Cross-product, transpose, and order
- Understanding neural networks through NumPy
- Neural networks using NumPy
- Processing batch of data
- Creating a multi-layer network
- Dense layers
- Activation functions
- Calculating loss through categorical cross-entropy loss
- Calculating accuracy s
- Conclusion
- 7. Derivatives, Backpropagation, and Optimizers
- Structure
- Weights Optimization
- Derivatives
- Partial Derivatives
- Backpropagation
- Optimizers: SGD, Adam, and so on
- Gradient-based optimization
- Momentum-based optimization
- RMSProp
- Adam
- Conclusion
- 8. Understanding Convolution and CNN Architectures
- Structure
- Intricacies of CNN
- Local Patterns and Global Patterns
- Spatial Hierarchies and Abstraction
- Convolution Operation and Feature Maps
- Pooling
- Padding
- Stride
- Introduction to CNN-based Networks
- Understanding the Complete Flow of CNN-based Network
- VGG16
- Inception Module: Naïve and Improved Version
- ResNet
- Other Variants of ResNet
- FractalNet and DenseNet
- Scaling Conv Networks: Efficient Net Architecture
- Different Types of Convolutions
- Depth-Separable Convolution
- Conclusion
- 9. Understanding Basics of TensorFlow and Keras
- Structure
- A Brief Look at Keras
- Understanding TensorFlow Internals
- Tensors
- Computational Graphs
- Operations (Ops)
- Automatic Differentiation
- Sessions
- Variables
- Eager Execution
- Layers and Models (Keras)
- TensorFlow vs. PyTorch vs. Theano
- TensorFlow vs. PyTorch
- TensorFlow vs. Theano
- TensorFlow: Layers, Activations, and More
- Types of Layers
- Dense Layer (Fully Connected Layer)
- Convolution Layer
- Max Pooling Layer
- Dropout Layer
- Recurrent Layer (LSTM)
- Embedding Layer
- Flatten Layer
- Batch Normalization Layer
- Global Average Pooling Layer
- Upsampling/Transposed Convolution Layer
- Activation Functions
- Optimizers
- Weight Initialization
- Loss Functions
- Multi-Input Single-Output Network with Custom Callbacks
- Conclusion
- 10. Building End-to-end Image Segmentation Pipeline
- Structure
- Fine-tuning and Interpretability
- Power of Fine-Tuning in Deep Learning
- SHAP - An Intuitive Way to Interpret Machine Learning Models
- Structuring Deep Learning Code
- Project Structure
- Python modules and packages
- Documentation
- Unit testing
- Debugging
- Logging
- Building End-to-end Segmentation Pipeline
- UNet and Attention Gates
- Config
- Dataloader
- Model building
- Understanding Attention block
- Executor
- Utils
- Evaluation
- main
- Conclusion
- 11. Latest Advancements in AI
- Structure
- Transformers: Improving NLP Using Attention
- Recurrent Neural Network (RNN)
- Long-Short Term Memory (LSTM)
- Self-Attention
- Example to understand the concept:
- Understanding Key, Query, and Value
- Example to understand the concept:
- Transformer Architecture
- ChatGPT/GPT Overview
- Object Detection: Understanding YOLO
- Object Detector Architecture Breakdown
- Backbone, Neck, and Head
- Bag of Freebies (BoF)
- CmBN: Cross-mini-Batch Normalization
- Bag of Specials (BoS)
- Cross-Stage Partial (CSP) Connection
- YOLO A rchitecture S election
- Spatial Pyramid Pooling (SPP)
- PAN Path - Aggregation Block
- Spatial Attention Module (SAM)
- Image Generation: GAN's and Diffusion models
- Generative Adversarial Networks
- Generative Discriminative models
- Variational Autoencoders
- GANs
- Diffusion Models
- DALL-E 2 Architecture
- The Encoder: Prior Diffusion Model
- The Decoder: GLIDE
- Conclusion
- Index
