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Welcome
Prerequisites
0. Environment Setup
Tutorials
1. Network’s Building Blocks
1.1. Convolution
1.2. Activation Functions
1.3. Pooling
1.4. Linear Layer
2. Quick Start
3. Optimisation and Learning
4. Vision
4.1. Image Classification
4.2. Image Segmentation
4.3. Transfer Learning
5. Other Modalities
5.1. Audio Classification
5.2. Text Classification
5.3. Language – Vision
6. Deep Generative Models
6.1. Generative Adversarial Network
6.2. Deep Autoencoder
6.3. Diffusion Probabilistic Model
6.4. Large Language Model
7. Interpretation Techniques
7.1. Activation Maps
7.2. Kernel Lesioning
7.3. Probing by linear classifiers
7.4. Representational Similarity Analysis
8. Big Projects
8.1. Python Scripting
8.2. TensorBoard
8.3. Working with Servers
9. Reinforcement Learning
Assignments
Warming-up
Optimisation and Learning
LLM Calculator
Zero-shot Evaluation
Student Projects
Deep Learning with Dobble
Complementary Materials
Python For Beginners
Data Types
Modules and NumPy Arrays
Conditions
Loops
Plotting
Functions
Modules and Objects
Inheritance
Repository
Open issue
Index
