1. Course Overview and System Setup FREE CHAPTER

• Course Overview
• PyTorch Introduction
• System Setup
• How to Get the Course Material
• Setting Up the conda Environment
• How to Work with the Course

2. Machine Learning

• Artificial Intelligence (101)
• Machine Learning (101)
• Machine Learning Models (101)

3. Deep Learning Introduction

• Deep Learning General Overview
• Deep Learning Modeling 101
• Performance
• From Perceptron to Neural Network
• Layer Types
• Activation Functions
• Loss Functions
• Optimizers
• Deep Learning Framework

4. Model Evaluation

• Underfitting Overfitting (101)
• Train Test Split (101)
• Resampling Techniques (101)

5. Neural Network from Scratch

• Section Overview
• Neural Network from Scratch (101)
• Calculating the dot-product (Coding)
• Neural Network from Scratch (Data Prep)
• Neural Network from Scratch Modeling __init__ Function
• Neural Network from Scratch Modeling Helper Functions
• Neural Network from Scratch Modeling Forward Function
• Neural Network from Scratch Modeling Backward Function
• Neural Network from Scratch Modeling Optimizer Function
• Neural Network from Scratch Modeling Train Function
• Neural Network from Scratch Model Training
• Neural Network from Scratch Model Evaluation

6. Tensors

• Section Overview
• From Tensors to Computational Graphs (101)
• Tensor (Coding)

7. PyTorch Modeling Introduction

• Section Overview
• Linear Regression from Scratch (Coding, Model Training)
• Linear Regression from Scratch (Coding, Model Evaluation)
• Model Class (Coding)
• Exercise: Learning Rate and Number of Epochs
• Solution: Learning Rate and Number of Epochs
• Batches (101)
• Batches (Coding)
• Datasets and Dataloaders (101)
• Datasets and Dataloaders (Coding)
• Saving and Loading Models (101)
• Saving and Loading Models (Coding)
• Model Training (101)
• Hyperparameter Tuning (101)
• Hyperparameter Tuning (Coding)

8. Classification Models

• Section Overview
• Classification Types (101)
• Confusion Matrix (101)
• ROC Curve (101)
• Multi-Class 1: Data Prep
• Multi-Class 2: Dataset Class (Exercise)
• Multi-Class 3: Dataset Class (Solution)
• Multi-Class 4: Network Class (Exercise)
• Multi-Class 5: Network Class (Solution)
• Multi-Class 6: Loss, Optimizer, and Hyperparameters
• Multi-Class 7: Training Loop
• Multi-Class 8: Model Evaluation
• Multi-Class 9: Naive Classifier
• Multi-Class 10: Summary
• Multi-Label (Exercise)
• Multi-Label (Solution)

9. CNN: Image Classification

• Section Overview
• CNNs (101)
• CNN (Interactive)
• Image Preprocessing (101)
• Image Preprocessing (Coding)
• Binary Image Classification (101)
• Binary Image Classification (Coding)
• Multi-Class Image Classification (Exercise)
• Multi-Class Image Classification (Solution)
• Layer Calculations (101)
• Layer Calculations (Coding)

10. CNN: Audio Classification

• Audio Classification (101)
• Audio Classification (Exercise)
• Audio Classification (Exploratory Data Analysis)
• Audio Classification (Data Prep-Solution)
• Audio Classification (Model-Solution)

11. CNN: Object Detection

• Section Overview
• Accuracy Metrics (101)
• Object Detection (101)
• Object Detection with detecto (Coding)
• Training a Model on GPU for Free (Coding)
• YOLO (101)
• Labeling Formats
• YOLOv7 Project (101)
• YOLOv7 Coding: Setup
• YOLOv7 Coding: Data Prep
• YOLOv7 Coding: Model Training
• YOLOv7 Coding: Model Inference
• YOLOv8 Coding: Model Training and Inference

12. Style Transfer

• Section Overview
• Style Transfer (101)
• Style Transfer (Coding)

13. Pre-Trained Networks and Transfer Learning

• Section Overview
• Transfer Learning and Pre-Trained Networks (101)
• Transfer Learning (Coding)

14. Recurrent Neural Networks

• Section Overview
• RNN (101)
• LSTM (Coding)
• LSTM (Exercise)

15. Recommender Systems

• Recommender Systems (101)
• RecSys (Coding 1/4) - Dataset and Model Class
• RecSys (Coding 2/4) - Model Training and Evaluation
• RecSys (Coding 3/4) - Users and Items
• RecSys (Coding 4/4) - Precision@k and Recall@k

16. Autoencoders

• Section Overview
• Autoencoders (101)
• Autoencoders (Coding)

17. Generative Adversarial Networks

• Section Overview
• GANs (101)
• GANs (Coding)
• GANs (Exercise)

18. Graph Neural Networks

• Graph Neural Networks (101)
• Graph Introduction (Coding)
• Node Classification (Coding: Data Prep)
• Node Classification (Coding: Model Train)
• Node Classification (Coding: Model Eval)

19. Transformers

• Transformers 101
• Vision Transformers (ViT)
• Train ViT on Custom Dataset (Coding)

20. PyTorch Lightning

• PyTorch Lightning (101)
• PyTorch Lightning (Coding)
• Early Stopping (101)
• Early Stopping (Coding)

21. Semi-Supervised Learning

• Semi-Supervised Learning (101)
• Supervised Learning (Reference Model, Coding)
• Semi-Supervised Learning (1/2: Dataset and Dataloader)
• Semi-Supervised Learning (2/2 Modeling)

22. Natural Language Processing (NLP)

• Natural Language Processing (101)
• Word Embeddings Intro (101)
• Sentiment OHE Coding Introduction
• Sentiment OHE (Coding)
• Word Embeddings with Neural Network (101)
• GloVe: Get Word Embedding (Coding)
• Glove: Find Closest Words (Coding)
• GloVe: Word Analogy (Coding)
• GloVe Word Cluster (101)
• GloVe Word (Coding)
• Sentiment with Embedding (101)
• Sentiment with Embedding (Coding)
• Apply Pre-Trained Natural Language Processing Models (101)
• Apply Pre-Trained Natural Language Processing Models (Coding)
• Vector Databases (101)
• Retrieval Augmented Generation (101)
• Claude 3 (101)
• Claude 3 (Coding)
• Zero-Shot Classification (101)
• Zero-Shot Classification (Coding)

23. Miscellaneous Topics

• OpenAI ChatGPT (101)
• ResNet (101)
• Inception (101)
• Inception Module (Coding)
• Extreme Learning (101)
• Extreme Learning (Coding)

24. Model Debugging

• Hooks (101)
• Hooks (Coding)

25. Model Deployment

• Model Deployment (101)
• Flask On-Premise, Hello World (Coding)
• API On-Premise with Deep Learning Model (Coding)
• API On-Premise: How to Consume the Data (Coding)
• Google Cloud: Deploy Model Weights (Coding)
• Google Cloud: Deploy REST API (Coding)

26. Final Section

• Thank You and Further Resources