DL eBook Fundamentals of Deep Learning
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Fundamentals of Deep Learning

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Unit I — Introduction to Deep Learning 3 chapters
  1. Chapter 1 — Deep Learning Landscape & Motivation
  2. Chapter 2 — Shallow ML Refresher: Linear Models
  3. Chapter 3 — Loss Functions & Optimization Basics
Unit II — Introduction to Neural Networks 3 chapters
  1. Chapter 4 — Biological Neuron → Computational Unit
  2. Chapter 5 — McCulloch–Pitts, Threshold Logic, Perceptron
  3. Chapter 6 — MLP & Backpropagation (incl. BPTT overview)
Unit III — Neural Networks in Practice 5 chapters
  1. Chapter 7 — Keras Essentials: Building Your First ANN
  2. Chapter 8 — Training Deep Networks: Regularization, Dropout, BatchNorm
  3. Chapter 9 — CNNs: Convolution, Pooling, Architectures
  4. Chapter 10 — RNN, LSTM, GRU: Sequence Modeling
  5. Chapter 11 — Transfer Learning & Fine-tuning
Unit IV — Deep Learning for NLP & Computer Vision 5 chapters
  1. Chapter 12 — NLP Foundations: Vector Space Semantics
  2. Chapter 13 — Word Embeddings: CBOW, Skip-gram, GloVe
  3. Chapter 14 — Vision Tasks: Segmentation, Detection, Captioning
  4. Chapter 15 — Generative Models: GANs (Framework)
  5. Chapter 16 — Attention Models + Video-to-Text (LSTM)

Deep Learning Lab Manual (AIML355)

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