All topics

Deep Learning

Neural networks, CNNs, RNNs, and transformers

44 chapters across 9 modules
1

Foundations of Deep Learning

4 chapters

1

What Is Deep Learning & Why It Works

6 min read

2

From Biological to Artificial Neurons

6 min read

3

A Brief History of Deep Learning

5 min read

4

The Deep Learning Landscape & Tooling

6 min read

2

Neural Networks From Scratch

6 chapters

1

The Perceptron & the Multi-Layer Perceptron

6 min read

2

Activation Functions

6 min read

3

Forward Propagation & Loss Functions

6 min read

4

Backpropagation Explained

7 min read

5

Gradient Descent & Its Variants

6 min read

6

Building a Neural Net From Scratch in NumPy

6 min read

3

Training Deep Networks Effectively

6 chapters

1

Weight Initialization

6 min read

2

Optimizers: SGD, Momentum, RMSProp, Adam

6 min read

3

Batch Normalization & Layer Normalization

6 min read

4

Regularization: Dropout, Weight Decay, Early Stopping

6 min read

5

Learning Rate Schedules & Warmup

6 min read

6

Vanishing/Exploding Gradients & Diagnosing Training Problems

6 min read

4

Convolutional Neural Networks

5 chapters

1

Convolution & Pooling Operations

7 min read

2

CNN Architectures

6 min read

3

Image Classification in Practice

6 min read

4

Object Detection & Segmentation

6 min read

5

Data Augmentation for Vision

5 min read

5

Sequence Models

5 chapters

1

Why Sequences Need Special Architectures

4 min read

2

Recurrent Neural Networks

6 min read

3

LSTMs & GRUs

6 min read

4

Sequence-to-Sequence & Encoder-Decoder Models

6 min read

5

Limitations That Motivate Attention

5 min read

6

Attention and Transformers

5 chapters

1

The Attention Mechanism

6 min read

2

Self-Attention & Multi-Head Attention

6 min read

3

Positional Encoding

6 min read

4

The Full Transformer Architecture

7 min read

5

Where Transformers Go From Here

5 min read

7

Transfer Learning and Practical Training

4 chapters

1

Why Train From Scratch Rarely Makes Sense

5 min read

2

Feature Extraction vs Fine-Tuning

6 min read

3

Pretrained Models & Model Hubs

5 min read

4

Practical Fine-Tuning Recipes

7 min read

8

Generative Deep Learning

4 chapters

1

Autoencoders

6 min read

2

Variational Autoencoders

6 min read

3

GANs

7 min read

4

Diffusion Models

6 min read

9

Dl Engineering and Capstone

5 chapters

1

PyTorch End-to-End Workflow

6 min read

2

GPUs, Mixed Precision & Scaling Training

6 min read

3

Debugging & Experiment Tracking

6 min read

4

Capstone: Building an Image Classifier End-to-End

6 min read

5

Where to Go Next

4 min read