Deep Learning - Computer Vision for Beginners Using PyTorch

Note: The course is primarily focused on teaching PyTorch and deep learning for computer vision, but it also includes a few sections on the fundamentals of Python (Sections 8–12). These optional learning sections are designed for individuals who may be new to Python or who want to refresh their knowledge of Python basics.

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 Matplotlib. 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.

All the resources for this course are available at: https://github.com/PacktPublishing/Deep-Learning---Computer-Vision-for-…

Type
video
Category
Data
publication date
2023-03-16
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

duration
434
key features
Learn how to perform Computer Vision tasks with deep learning * Learn to implement LeNet architecture on CIFAR10 dataset, which has 60,000 images * Build your programming foundation with Python
approach
This is a step-by-step learning course where we will start with the basics and move toward real-world implementation. You will also learn the basics of Python programming and build a mini project to test our learning in the real world.
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.
meta description
Acquire fundamental knowledge in Computer Vision with PyTorch and Python, supplemented by expert guidance and practical tips on utilizing convolutional neural networks and deep learning techniques
short description
In this course, you will be learning one of the widely used deep learning frameworks, that is, PyTorch, and learn the basics of convolutional neural networks in PyTorch. We will also cover the basics of Python and understand how to implement different Python libraries.
subtitle
Learn the basics of Computer Vision in PyTorch and Python with expert tips on convolutional neural network deep learning
keywords
PyTorch, Python, NumPy, Pandas, Matplotlib, LeNet, convolutional neural networks, neural networks, Deep learning, GPU, AutoGrad
Product ISBN
9781837634286