PyTorch for Deep Learning and Computer Vision [Video]

More Information
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
About

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. 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.

All the code and supporting files for this course are available at: https://github.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision

Style and Approach

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.

Features
  • This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.
  • No experience required.
Course Length 12 hours 32 minutes
ISBN 9781838822804
Date Of Publication 2 Apr 2019

Authors

Rayan Slim

Rayan Slim - Teacher

Rayan is a full-time software developer based in Ottawa, Canada. He is the first ventured into development when working on a start-up. Since then, he has built countless web and mobile applications as a freelance developer, meanwhile expanding his repertoire and exploring new avenues in Robotics, Deep Learning & Data Analytics. In his free time, he loves to teach!

Jad Slim

Jad Slim - Data Science Entrepreneur

Jad studied mechanical engineering at the University of Ottawa. Jad also has experience in machine learning, computer vision, mathematical modeling, computer simulation, and intelligent systems. He has also developed many deep learning applications, and is currently pursuing an interest in autonomous machines. Skilled in deep learning libraries such as Tensorflow, Keras and MATLAB.

Amer Sharaf

Amer Sharaf - Developer

Amer is a full-time developer with a specialized interest in Artificial intelligence (AI). AI is now taking on more sophisticated roles that can truly amplify human capabilities. With a background in Mechanical Engineering and computer science, he has always looked for ways to use the power of AI to create practical solutions that revolutionize the way we live. He aims to make artificial intelligence more accessible to all students, no matter the skill level! 

Sarmad Tanveer

Sarmad Tanveer - Data Scientist

Sarmad has a deep passion for data science. He is a Mechanical Engineering graduate turned Data Scientist and had gained experience in the field while working on his very own startups. His main work focuses on creating predictive models using a combination of complex deep learning algorithms and sentiment analysis. He also has prior experience with deep learning fueled autonomous machines. In his spare time, he enjoys teaching courses and sharing his knowledge with all of you!