Computer Vision with PyTorch Online Course

Computer Vision with PyTorch Online Course

This course is designed to teach PyTorch and deep learning for computer vision, with optional sections covering Python fundamentals (Sections 8–12) for beginners or those needing a refresher. You’ll start by learning PyTorch basics, including the AutoGrad feature, and how to leverage free GPUs for model training. The course then guides you through building deep learning models, exploring convolutional neural networks (CNNs), and applying them to real-world datasets. Alongside, you’ll revisit core Python skills, work with libraries like NumPy, Pandas, and Matplotlib, and complete a mini-project by building a Hangman game in Python. By the end, you’ll be equipped to perform computer vision tasks using deep learning with PyTorch.

Who should take this Course?

The Computer Vision with PyTorch Online Course is ideal for data scientists, machine learning engineers, AI enthusiasts, and software developers who want to master image processing and computer vision techniques using PyTorch. It is also well-suited for students, researchers, and professionals in fields like robotics, healthcare, and automation who are looking to apply deep learning and computer vision to solve real-world challenges.

What you will learn

• Learn how to work with PyTorch
• Build intuition on convolution operation on images
• Implement gradient descent using AutoGrad
• Learn about LeNet architecture
• Create a mini-Python project game
• Understand how to use NumPy, Pandas, and Matplotlib libraries

Course Outline

Welcome Aboard

• Course Introduction
• Why Is PyTorch Powerful?

Introduction to PyTorch and Tensors

• What Is PyTorch
• Diving into PyTorch

Installing PyTorch

• Create Tensors in PyTorch
• Tensor Slicing and Reshape
• Mathematical Operations on Tensors
• NumPy in PyTorch
• What Is CUDA
• PyTorch on GPU

AutoGrad in PyTorch

• AutoGrad in PyTorch
• AutoGrad in a Loop

Creating Deep Neural Networks in PyTorch

• Building the First Neural Network
• Writing a Deep Neural Network
• Writing a Custom NN Module

CNN in PyTorch

• Data Loading - CIFAR10
• Data Visualization
• CNN Recap
• First CNN
• CNN Deep Layers

LeNet Architecture in PyTorch

• LeNet Overview
• LeNet Model in PyTorch
• Preparation and Evaluation

Optional Learning - Python Basics

• Why Learn Any Programming Language
• Why Choose Python
• Installing Jupyter Notebook
• Jupyter Notebook - Tips and Tricks
• What We Will Cover in This Section
• Variables in Python
• Print Function
• Numerical Data Types and Arithmetic Operations in Python
• String Data Type
• Boolean Data Type
• Type Conversion and Type Casting
• Adding Comments in Python Programming Language
• Data Structures in Python
• Tuples and Sets in Python
• Python Dictionaries
• Conditional Statements in Python - if
• Conditional Statements in Python - While
• Inbuilt Functions in Python - range and input
• For Loops
• Functions in Python
• Classes in Python

Optional Learning - Mini Project with Python Basics

• Mini Project - Hangman
• Writing a Class
• Mini Project - Continued
• Logic Building
• Logic for Single-Letter input
• Final Testing

Optional Learning - Python for Data Science – with NumPy

• NumPy
• Resize and Reshape Arrays
• Slicing
• Broadcasting
• Mathematical Operations and Functions in NumPy

Optional Learning - Python for Data Science – with Pandas

• Pandas Library
• Pandas Dataframe
• Pandas Dataframe - Load from External File
• Working with Null Values
• Slicing Pandas Dataframe
• Imputation

Optional Learning - Python for Data Science – with Matplotlib

• Matplotlib Introduction
• Format the Plot
• Plot Formatting and Scatter Plot
• Histplot