ANN RNN LSTM and Transformers: Artificial Neural Networks, Recurrent Neural Networks, LSTM & Transformers: A Practical Guide to Deep Learning, NLP and Generative AI

Starting with the fundamentals of Artificial Neural Networks (ANNs), the book explains how neural networks learn from data through forward propagation, backpropagation, activation functions, and optimization techniques. Readers will build a strong foundation before moving to more advanced architectures.

The book then explores Recurrent Neural Networks (RNNs), explaining how sequential data is processed for applications such as text generation, language translation, speech recognition, and time-series forecasting. It also discusses the limitations of traditional RNNs, including the vanishing gradient problem.

To overcome these challenges, the book introduces Long Short-Term Memory (LSTM) networks, demonstrating how memory cells and gating mechanisms enable models to capture long-term dependencies in sequential data. Practical examples illustrate the use of LSTMs in sentiment analysis, stock price prediction, machine translation, and chatbot development.

The final section covers the revolutionary Transformer architecture, which has transformed the field of Natural Language Processing (NLP) and Generative AI. Readers will learn about self-attention, positional encoding, multi-head attention, encoder-decoder architecture, and the foundation behind modern Large Language Models (LLMs) such as GPT, BERT, and other state-of-the-art AI systems.

The book combines theoretical concepts with practical implementation using Python, NumPy, TensorFlow, Keras, and PyTorch, enabling readers to build real-world deep learning applications from scratch.

What You'll Learn

Fundamentals of Artificial Neural Networks (ANN)

Perceptron and Multilayer Neural Networks

Forward Propagation and Backpropagation

Activation Functions and Loss Functions

Gradient Descent and Optimization Algorithms

Recurrent Neural Networks (RNN)

Sequence Modeling and Time-Series Analysis

Vanishing and Exploding Gradient Problems

Long Short-Term Memory (LSTM) Architecture

Gated Recurrent Unit (GRU)

Attention Mechanism

Transformer Architecture

Self-Attention and Multi-Head Attention

Positional Encoding

Encoder and Decoder Models

BERT, GPT, and Large Language Models

Natural Language Processing Applications

Machine Translation and Text Summarization

Sentiment Analysis and Text Classification

Practical Deep Learning Projects

Model Evaluation and Performance Optimization