Video description
Learn to build highly sophisticated deep learning and Computer Vision applications with PyTorch
About This Video
- This course is designed to help you become an accomplished deep learning developer even with no experience in programming or mathematics
In Detail
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 of the deep learning domain with its flexibility and has made building deep learning models easier. The development world offers some of the highest paying jobs in deep learning. In this exciting course, instructor Rayan Slim will help you learn and master deep learning with PyTorch. Having taught over 44,000 students, Rayan is a highly rated and experienced instructor who has followed a learning-by-doing style to create this course. You'll go from a beginner to deep learning expert with your instructor completing each step of the task with you. By the end of this 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 organization.
Who this book is for
This course is for you if you’re interested in deep learning and Computer Vision. Anyone (no matter the skill level) who wants to transition into the field of artificial intelligence and entrepreneurs with an interest in working on some of the most cutting-edge technologies will find this course useful.
Publisher resources
Table of contents
- Chapter 1 : Introduction
- Chapter 2 : Getting Started
- Chapter 3 : Intro to Tensors â PyTorch
- Chapter 4 : Linear Regression â PyTorch
- Chapter 5 : Perceptrons â PyTorch
- Chapter 6 : Deep Neural Networks â PyTorch
- Chapter 7 : Image Recognition â PyTorch
- Chapter 8 : Convolutional Neural Networks â PyTorch
- Chapter 9 : CIFAR 10 Classification â PyTorch
- Chapter 10 : Transfer Learning â PyTorch
- Chapter 11 : Style Transfer â PyTorch
-
Chapter 12 : Appendix A - Python Crash Course
- Overview
- Anaconda Installation (Mac)
- Anaconda Installation Windows
- Jupyter Notebooks
- Arithmetic Operators
- Variables
- Numeric Data Types
- String
- Booleans
- Methods
- Lists
- Slicing
- Membership Operator
- Mutability
- Mutability II
- Common Functions Methods
- Tuples
- Sets
- Dictionaries
- Compound Data Structures
- Part 1 â Outro
- Part 2 - Control Flow
- If, else
- elseif
- Complex Comparisons
- For Loops
- For Loops II
- While Loops
- Break
- Part 2 â Outro
- Part 3 â Functions
- Functions
- Scope
- Doc Strings
- Lambda and Higher Order Functions
- Part 3 â Outro
- Chapter 13 : Appendix B - NumPy Crash Course
- Chapter 14 : Appendix C - Softmax Explanation
Product information
- Title: PyTorch for Deep Learning and Computer Vision
- Author(s):
- Release date: April 2019
- Publisher(s): Packt Publishing
- ISBN: 9781838822804
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