PyTorch for Deep Learning and Computer Vision

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

Download Example Code

Table of contents

  1. Chapter 1 : Introduction
    1. Introduction
  2. Chapter 2 : Getting Started
    1. Finding the codes (Github)
    2. A Look at the Projects
  3. Chapter 3 : Intro to Tensors – PyTorch
    1. Intro
    2. 1 Dimensional Tensors
    3. Vector Operations
    4. 2 Dimensional Tensors
    5. Slicing 3D Tensors
    6. Matrix Multiplication
    7. Gradient with PyTorch
    8. Outro
  4. Chapter 4 : Linear Regression – PyTorch
    1. Intro
    2. Making Predictions
    3. Linear Class
    4. Custom Modules
    5. Creating Dataset
    6. Loss Function
    7. Gradient Descent
    8. Mean Squared Error
    9. Training - Code Implementation
    10. Outro
  5. Chapter 5 : Perceptrons – PyTorch
    1. Intro
    2. What is Deep Learning
    3. Creating Dataset
    4. Perceptron Model
    5. Model Setup
    6. Model Training
    7. Model Testing
    8. Outro
  6. Chapter 6 : Deep Neural Networks – PyTorch
    1. Intro
    2. Non-Linear Boundaries
    3. Architecture
    4. Feedforward Process
    5. Error Function
    6. Backpropagation
    7. Code Implementation
    8. Testing Model
    9. Outro
  7. Chapter 7 : Image Recognition – PyTorch
    1. Intro
    2. MNIST Dataset
    3. Training and Test Datasets
    4. Image Transforms
    5. Neural Network Implementation
    6. Neural Network Validation
    7. Final Tests
    8. A note on adjusting batch size
    9. Outro
  8. Chapter 8 : Convolutional Neural Networks – PyTorch
    1. Convolutions and MNIST
    2. Convolutional Layer
    3. Convolutions II
    4. Pooling
    5. Fully Connected Network
    6. Neural Network Implementation with PyTorch
    7. Model Training with PyTorch
  9. Chapter 9 : CIFAR 10 Classification – PyTorch
    1. The CIFAR 10 Dataset
    2. Testing LeNet
    3. Hyperparameter Tuning
    4. Data Augmentation
  10. Chapter 10 : Transfer Learning – PyTorch
    1. Pre-trained Sophisticated Models
    2. AlexNet and VGG16
  11. Chapter 11 : Style Transfer – PyTorch
    1. VGG 19
    2. Image Transforms
    3. Feature Extraction
    4. The Gram Matrix
    5. Optimization
    6. Style Transfer with Video
  12. Chapter 12 : Appendix A - Python Crash Course
    1. Overview
    2. Anaconda Installation (Mac)
    3. Anaconda Installation Windows
    4. Jupyter Notebooks
    5. Arithmetic Operators
    6. Variables
    7. Numeric Data Types
    8. String
    9. Booleans
    10. Methods
    11. Lists
    12. Slicing
    13. Membership Operator
    14. Mutability
    15. Mutability II
    16. Common Functions Methods
    17. Tuples
    18. Sets
    19. Dictionaries
    20. Compound Data Structures
    21. Part 1 – Outro
    22. Part 2 - Control Flow
    23. If, else
    24. elseif
    25. Complex Comparisons
    26. For Loops
    27. For Loops II
    28. While Loops
    29. Break
    30. Part 2 – Outro
    31. Part 3 – Functions
    32. Functions
    33. Scope
    34. Doc Strings
    35. Lambda and Higher Order Functions
    36. Part 3 – Outro
  13. Chapter 13 : Appendix B - NumPy Crash Course
    1. Overview
    2. Arrays vs Lists
    3. Multidimensional Arrays
    4. One Dimensional Slicing
    5. Reshaping
    6. Multidimensional Slicing
    7. Manipulating Array Shapes
    8. Matrix Multiplication
    9. Stacking
    10. Outro
  14. Chapter 14 : Appendix C - Softmax Explanation
    1. Softmax
    2. Cross Entropy

Product information

  • Title: PyTorch for Deep Learning and Computer Vision
  • Author(s): Rayan Slim, Jad Slim, Amer Sharaf, Sarmad Tanveer
  • Release date: April 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781838822804