ANN, RNN, LSTM and Transformers is a practical and comprehensive guide designed for students, developers, Machine Learning learners, and AI enthusiasts who want to understand the evolution of modern deep learning architectures.

The book begins with the fundamentals of Artificial Neural Networks (ANNs) and gradually progresses to Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Attention Mechanisms, and Transformer architectures.

Readers will learn how neural networks process data, how sequence models handle sequential information, how LSTM networks address limitations of traditional RNNs, and how Transformers revolutionized Natural Language Processing and Generative AI.

With a hands-on and practical approach, the book explains important concepts along with applications, making it useful for both learning and interview preparation.

What You Will Learn
Introduction to Artificial Neural Networks
Neural Network Architecture
Neurons, Weights, and Bias
Activation Functions
Forward Propagation
Backpropagation
Loss Functions and Optimization
Gradient Descent
Training Neural Networks
Introduction to Deep Learning
Recurrent Neural Networks (RNN)
RNN Architecture and Working
Sequence Data Processing
Vanishing and Exploding Gradient Problems
Long Short-Term Memory (LSTM)
LSTM Architecture and Gates
Forget, Input, and Output Gates
GRU Fundamentals
Sequence Prediction
Time-Series Applications
Natural Language Processing Applications
Attention Mechanism
Self-Attention
Encoder and Decoder Architecture
Introduction to Transformers
Transformer Architecture
Positional Encoding
Multi-Head Attention
Feed-Forward Networks
Transformer Encoder and Decoder
BERT and GPT Concepts
Transformers for NLP
Transformers for Generative AI
Transfer Learning and Fine-Tuning
PyTorch and TensorFlow Fundamentals
Model Training and Evaluation
Real-World Deep Learning Projects