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)