RAIN
LMS login Apply
Cover of AIML 212 Deep Learning

AIML 212

Deep Learning

Neural Networks, Computer Vision, Language Models and Generative AI

Deep learning is the technology behind most of what the public now calls artificial intelligence: phones that recognise faces, apps that diagnose crop diseases from a photograph, systems that transcribe and translate speech, and assistants that write, summarise and answer questions. All of them rest on one idea, developed in this book: networks of simple units, arranged in many layers and trained from data by gradient descent, can learn representations of images, sound and text that no programmer could write by hand.

14 chapters in 4 parts, 294 pages. Editor-in-Chief: Olusola Sayeed Ayoola. Published by RAIN, Ibadan, 2026.

Buy this book Look inside Read it (owners and RAIN trainees) Certification

Contents

Open a chapter to see its sections. Each chapter ends with a QR code for its free assessment.

1From Neurons to Networks
  • 1.1 The artificial neuron and the perceptron
  • 1.2 Multilayer perceptrons
  • 1.3 Activation functions
  • 1.4 Universal approximation and the value of depth
  • 1.5 Forward propagation in matrix form
  • 1.6 Implementation

Take the Chapter 1 assessment

2Backpropagation
  • 2.1 Computational graphs
  • 2.2 Backpropagation in a multilayer perceptron
  • 2.3 Vanishing and exploding gradients
  • 2.4 A two-layer network from scratch
  • 2.5 Automatic differentiation

Take the Chapter 2 assessment

3Training Deep Networks
  • 3.1 Loss functions
  • 3.2 Optimisers
  • 3.3 Initialisation and normalisation
  • 3.4 Regularisation
  • 3.5 Workflows: PyTorch, Keras and GPUs
  • 3.6 Experiments

Take the Chapter 3 assessment

4Convolutional Neural Networks
  • 4.1 Convolution
  • 4.2 Padding, stride, pooling and receptive fields
  • 4.3 Implementing and differentiating convolution
  • 4.4 Architectures
  • 4.5 Transfer learning and fine-tuning
  • 4.6 Case study: a cassava disease classifier

Take the Chapter 4 assessment

5Computer Vision Techniques
  • 5.1 Images and OpenCV basics
  • 5.2 Object detection
  • 5.3 Segmentation
  • 5.4 Evaluating detectors
  • 5.5 Case studies

Take the Chapter 5 assessment

6Generative Adversarial Networks and Diffusion Models
  • 6.1 The GAN game
  • 6.2 Why GAN training is hard
  • 6.3 Diffusion models
  • 6.4 Case study: generating from a known distribution

Take the Chapter 6 assessment

7Recurrent Networks
  • 7.1 Recurrent neural networks
  • 7.2 Backpropagation through time
  • 7.3 LSTM and GRU
  • 7.4 Sequence-to-sequence models
  • 7.5 Case studies

Take the Chapter 7 assessment

8Natural Language Processing
  • 8.1 Text preprocessing and tokenisation
  • 8.2 Bag-of-words, TF-IDF and text classification
  • 8.3 Word embeddings
  • 8.4 Sequence labelling and named-entity recognition
  • 8.5 NLP for Nigerian languages
  • 8.6 Case studies

Take the Chapter 8 assessment

9Transformers and Attention
  • 9.1 Attention as a differentiable lookup
  • 9.2 Multi-head attention, masking and the transformer block
  • 9.3 Positional encoding
  • 9.4 Transformer architectures
  • 9.5 Complexity and efficient attention
  • 9.6 Case studies

Take the Chapter 9 assessment

10Large Language Models
  • 10.1 Pretraining
  • 10.2 Decoding
  • 10.3 Fine-tuning
  • 10.4 Instruction tuning and alignment
  • 10.5 Retrieval-augmented generation
  • 10.6 Tools and agents
  • 10.7 Evaluating language-model systems
  • 10.8 Case studies

Take the Chapter 10 assessment

11Explainable AI
  • 11.1 What is an explanation?
  • 11.2 LIME
  • 11.3 Shapley values
  • 11.4 Gradient-based attributions
  • 11.5 The limits of explanation
  • 11.6 Case studies

Take the Chapter 11 assessment

12Generative AI Applications
  • 12.1 Text generation and prompting
  • 12.2 Image generation
  • 12.3 Audio and speech
  • 12.4 Watermarking and provenance
  • 12.5 Copyright, consent and misuse
  • 12.6 Case studies

Take the Chapter 12 assessment

13Deploying Deep Learning Models
  • 13.1 Model formats
  • 13.2 Serving models
  • 13.3 Models on phones and small devices
  • 13.4 Monitoring and drift
  • 13.5 Deploying in Nigerian conditions
  • 13.6 Case studies

Take the Chapter 13 assessment

14Responsible AI
  • 14.1 Fairness
  • 14.2 Privacy
  • 14.3 Safety and robustness
  • 14.4 Governance and documentation
  • 14.5 Case studies

Take the Chapter 14 assessment

Get a copy

Buy AIML 212 as an e-book you read on the RAIN platform, or as a softcover or hardcover delivered anywhere in Nigeria and abroad. Schools can order many copies at once.

Prices and ordering in the RAIN bookshop

Other RAIN textbooks

AIML 101 Building and Deploying AI ApplicationsAIML 111 Data Science with PythonAIML 112 Machine Learning IAIML 211 Machine Learning IIRDA 111 Product Design and DevelopmentRDA 112 Practical Electronics

All the books