PyTorch for Deep Learning and Computer Vision

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Additional FREE Content

Chapter 2: Getting Started

Lecture 1: Finding the codes (Github)

Lecture 2: A Look at the Projects

Chapter 3: Intro to Tensors – PyTorch

Lecture 1: Intro

Lecture 2: 1 Dimensional Tensors

Lecture 3: Vector Operations

Lecture 4: 2 Dimensional Tensors

Lecture 5: Slicing 3D Tensors

Lecture 6: Matrix Multiplication

Lecture 7: Gradient with PyTorch

Lecture 8: Outro

Chapter 4: Linear Regression – PyTorch

Lecture 1: Intro

Lecture 2: Making Predictions

Lecture 3: Linear Class

Lecture 4: Custom Modules

Lecture 5: Creating Dataset

Lecture 6: Loss Function

Lecture 7: Gradient Descent

Lecture 8: Mean Squared Error

Lecture 9: Training – Code Implementation

Lecture 10: Getting Weird Results?

Lecture 11: Outro

Lecture 12: Summary

Chapter 5: Perceptrons – PyTorch

Lecture 1: Intro

Lecture 2: What is Deep Learning

Lecture 3: Creating Dataset

Lecture 4: Perceptron Model

Lecture 5: Model Setup

Lecture 6: Model Training

Lecture 7: Model Testing

Lecture 8: Outro

Chapter 6: Deep Neural Networks – PyTorch

Lecture 1: Intro

Lecture 2: Non-Linear Boundaries

Lecture 3: Architecture

Lecture 4: Feedforward Process

Lecture 5: Error Function

Lecture 6: Backpropagation

Lecture 7: Code Implementation

Lecture 8: Testing Model

Lecture 9: Outro

Chapter 7: Image Recognition – PyTorch

Lecture 1: Intro

Lecture 2: MNIST Dataset

Lecture 3: Training and Test Datasets

Lecture 4: Important Update – Bug fix for next lesson

Lecture 5: Image Transforms

Lecture 6: Neural Network Implementation

Lecture 7: Neural Network Validation

Lecture 8: Test Links

Lecture 9: Final Tests

Lecture 10: A note on adjusting batch size

Lecture 11: Outro

Chapter 8: Convolutional Neural Networks – PyTorch

Lecture 1: Convolutions and MNIST

Lecture 2: Convolutional Layer

Lecture 3: Convolutions II

Lecture 4: Pooling

Lecture 5: Fully Connected Network

Lecture 6: Neural Network Implementation with PyTorch

Lecture 7: Model Training with PyTorch

Chapter 9: CIFAR 10 Classification – PyTorch

Lecture 1: The CIFAR 10 Dataset

Lecture 2: Testing LeNet

Lecture 3: Hyperparameter Tuning

Lecture 4: Data Augmentation

Chapter 10: Transfer Learning – PyTorch

Lecture 1: Pre-trained Sophisticated Models

Lecture 2: Github Link for Dataset

Lecture 3: AlexNet and VGG16

Chapter 11: Style Transfer – PyTorch

Lecture 1: Recommended Paper to Read (Optional)

Lecture 2: VGG 19

Lecture 3: Images Required for Next Lesson (Resource)

Lecture 4: Image Transforms

Lecture 5: Feature Extraction

Lecture 6: 2nd Optional Paper to Read

Lecture 7: The Gram Matrix

Lecture 8: Optimization

Lecture 9: Content and Style Images

Lecture 10: Style Transfer with Video

Lecture 11: Goodbye, for now

Chapter 12: All Source Codes

Lecture 1: Intro

Lecture 2: Linear Regression

Lecture 3: Logistic Regression

Lecture 4: Deep Neural Networks

Lecture 5: MNIST Classification

Lecture 6: Convolutional Neural Networks

Lecture 7: CIFAR 10

Lecture 8: Transfer Learning

Lecture 9: Style Transfer

Chapter 13: Appendix A – Python Crash Course (Optional)

Lecture 1: Python Crash Course – Free Access