PyTorch Deep Learning Online Course

PyTorch Deep Learning Online Course

About the Course

PyTorch is a powerful and widely-used Python framework for building and deploying deep learning models. Known for its flexibility and ease of use, PyTorch has become a go-to tool for researchers and developers in the field of artificial intelligence.

In this course, you'll gain a solid understanding of key deep learning concepts and learn how to implement machine learning models using PyTorch. Topics include:

• Neural Networks and Tensors
• Classification models
• Convolutional Neural Networks (CNNs)
• Natural Language Processing (NLP)

Who should take this course?

This course is ideal for developers, data enthusiasts, aspiring data scientists, machine learning engineers, and AI professionals. Whether you're a complete beginner or have some experience, the course starts from the basics and progresses to advanced deep learning topics—making it suitable for learners at any level.

Course Curriculum

Course Overview and System Setup

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

Machine Learning

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

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

Model Evaluation

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

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

Tensors

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

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)

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)

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)

CNN: Audio Classification

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

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

Style Transfer

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

Recurrent Neural Networks

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

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

Autoencoders

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

Generative Adversarial Networks

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

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)

Transformers

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

PyTorch Lightning

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

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)

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)

Miscellaneous Topics

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

Model Debugging

• Hooks (101)
• Hooks (Coding)

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)