In this course, we will take a step-by-step method by first grasping PyTorch's fundamentals. Then, using a guide to getting free GPU for learning, you will learn how to code in GPU. You will then learn about PyTorch's AutoGrad feature and how to use it. Later, you will learn how to use PyTorch to create deep learning models and understand the fundamentals of convolutional neural networks (CNN). You will also learn how to use CNN with a real-world dataset.
Additionally, the course will emphasize the fundamentals and lay the groundwork for an understanding of Python. We will also talk about the three significant Python libraries known as NumPy, Pandas, and MatDescriptionlib. In this part of the course, we will also build a mini project where we will be building a hangman game in Python.
By the end of this course, we will be able to perform Computer Vision tasks with deep learning.
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 MatDescriptionlib libraries
Audience
Software developers, machine learning practitioners, data scientists, and anybody else interested in understanding PyTorch and deep learning should take this course. While a basic knowledge of Python would be beneficial, it is not a prerequisite as we will be covering the necessary fundamentals during the course.