Deep learning Complete Book

📖 Chapter 1: Introduction to Deep Learning

• What is Deep Learning?
• Difference between Machine Learning and Deep Learning
• Applications of Deep Learning
• History and Evolution

📖 Chapter 2: Mathematical Foundations

• Linear Algebra Basics (Matrix, Vectors, Tensors)
• Calculus (Derivatives, Gradients)
• Probability and Statistics
• Chain Rule and Partial Derivatives

📖 Chapter 3: Neural Networks Basics

• Biological Neuron vs Artificial Neuron
• Perceptron and Multi-layer Perceptron (MLP)
• Activation Functions (Sigmoid, Tanh, ReLU)
• Loss Functions and Optimization

📖 Chapter 4: Backpropagation and Training

• Forward Pass and Loss Calculation
• Backward Pass (Gradient Descent)
• Learning Rate, Epochs, Batches
• Overfitting, Underfitting and Regularization

📖 Chapter 5: Deep Neural Networks (DNN)

• Architecture of DNN
• Vanishing/Exploding Gradients
• Weight Initialization Techniques
• Batch Normalization

📖 Chapter 6: Convolutional Neural Networks (CNNs)

• What is Convolution?
• CNN Layers (Convolution, Pooling, Flatten, Dense)
• Use in Image Classification
• Famous CNN Architectures (LeNet, AlexNet, VGG, ResNet)

📖 Chapter 7: Recurrent Neural Networks (RNNs)

• Introduction of RNN
• Sequence Data and Time-Series
• RNN Structure
• Vanishing Gradient Problem in RNNs
• LSTM and GRU Networks

📖 Chapter 8: Transfer Learning & Pretrained Models

• What is Transfer Learning?
• Feature Extraction vs Fine Tuning
• Pretrained Models (VGG, ResNet, Inception, BERT)

📖 Chapter 9: Autoencoders & Representation Learning

• What is an Autoencoder?
• Encoder-Decoder Structure
• Variational Autoencoders (VAE)
• Applications: Denoising, Dimensionality Reduction

📖 Chapter 10: Generative Adversarial Networks (GANs)

• Introduction to GANs
• Generator vs Discriminator
• Training Process of GAN
• DCGAN, StyleGAN

📖 Chapter 11: Deep Reinforcement Learning

• Basics of Reinforcement Learning
• Q-Learning
• Deep Q-Network (DQN)
• Applications in Games and Robotics

📖 Chapter 12: Natural Language Processing with Deep Learning

• Introduction of NLP
• Word Embeddings (Word2Vec, GloVe)
• Sequence Models for Text (RNN, LSTM)
• Transformers and BERT

📖 Chapter 13: Frameworks and Tools

• TensorFlow and Keras Basics
• PyTorch Basics
• Model Training, Saving, and Loading in PyTroch
• Model Training, Saving, and Loading in Keras
• Using GPUs/TPUs

📖 Chapter 14: Model Evaluation and Deployment

• what is Model Evaluation and Deployment
• Evaluation Metrics ,Confusion Matrix, Precision, Recall
• Saving & Exporting Models
• Serving via API / Web App

📖 Chapter 15: Research Trends and Future Scope

• Self-Supervised Learning
• Diffusion Models
• Explainable AI (XAI)
• Deep Learning in Real World Applications