Udemy

PyTorch for Deep Learning and Computer Vision

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  • 14,336 Students
  • Updated 12/2025
4.8
(2,192 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
14 Hour(s) 13 Minute(s)
Language
English
Rating
4.8
(2,192 Ratings)
6 views

Course Overview

PyTorch for Deep Learning and Computer Vision

Build Highly Sophisticated Deep Learning and Computer Vision Applications with PyTorch

PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models.

Deep Learning jobs command some of the highest salaries in the development world. This course is meant to take you from the complete basics, to building state-of-the art Deep Learning and Computer Vision applications with PyTorch.

Learn & Master Deep Learning with PyTorch in this fun and exciting course with top instructor Rayan Slim. With over 44000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.

You'll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen.

By the end of the course, you will have built state-of-the art Deep Learning and Computer Vision applications with PyTorch. The projects built in this course will impress even the most senior developers and ensure you have hands on skills that you can bring to any project or company.

This course will show you to:

  • Learn how to work with the tensor data structure

  • Implement Machine and Deep Learning applications with PyTorch

  • Build neural networks from scratch

  • Build complex models through the applied theme of advanced imagery and Computer Vision

  • Learn to solve complex problems in Computer Vision by harnessing highly sophisticated pre-trained models

  • Use style transfer to build sophisticated AI applications that are able to seamlessly recompose images in the style of other images.

No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.

This course also comes with all the source code and friendly support in the Q&A area.

Who this course is for:

  • Anyone with an interest in Deep Learning and Computer Vision

  • Anyone (no matter the skill level) who wants to transition into the field of Artificial Intelligence

  • Entrepreneurs with an interest in working on some of the most cutting edge technologies

  • All skill levels are welcome!

Course Content

  • 15 section(s)
  • 102 lecture(s)
  • Section 1 Introduction
  • Section 2 Getting Started
  • Section 3 Intro to Tensors - PyTorch
  • Section 4 Linear Regression - PyTorch
  • Section 5 Perceptrons - PyTorch
  • Section 6 Deep Neural Networks - PyTorch
  • Section 7 Image Recognition - PyTorch
  • Section 8 Convolutional Neural Networks - PyTorch
  • Section 9 CIFAR 10 Classification - PyTorch
  • Section 10 Transfer Learning - PyTorch
  • Section 11 Style Transfer - PyTorch
  • Section 12 All Source Codes
  • Section 13 Appendix A - Python Crash Course (Optional)
  • Section 14 Appendix B - NumPy Crash Course (Optional)
  • Section 15 Appendix C - Softmax Explanation (Optional)

What You’ll Learn

  • Implement Machine and Deep Learning applications with PyTorch, Build Neural Networks from scratch, Build complex models through the applied theme of Advanced Imagery and Computer Vision, Solve complex problems in Computer Vision by harnessing highly sophisticated pre-trained models, Use style transfer to build sophisticated AI applications


Reviews

  • J
    Juan Carlos Carlos Alberto
    5.0

    A little hard to understand, but very helpful.

  • W
    Wang Leong Wong
    4.5

    Thank you. Style Transfer is the final boss of this course!

  • C
    Chandankumar Patel
    5.0

    Excellent Course

  • W
    Wendy Melissa Rivera Ayllón
    5.0

    aprendi

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