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Welcome
Prerequisites
0. Environment Setup
Tutorials
1. Network’s Building Blocks
1.1. Convolution
1.2. Pooling
1.3. Activation Function
2. Quick Start
3. Optimisation and Learning
4. Vision
4.1. Image Classification
4.2. Image Segmentation
5. Deep Generative Models
5.1. Generative Adversarial Networks
5.2. Deep Autoencoders
5.3. Diffusion Probabilistic Models
6. Interpretation Techniques
6.1. Activation Maps
6.2. Kernel Lesioning
6.3. Probing by linear classifiers
7. Big Projects
7.1. Python Scripting
7.2. TensorBoard
7.3. Working with Servers
8. Other Modalities
8.1. Audio Classification
8.2. Text Classification
8.3. Language – Vision
9. Reinforcement Learning
Assignments
1. Warming-up
2. Dataloaders
3. Optimisation and Learning
Student projects
Deep Learning with Dobble
Python course
Beginners
Data Types
Modules and NumPy Arrays
Conditions
Loops
Plotting
Functions
Repository
Open issue
Index