Hands-On Computer Vision with PyTorch 1.x

Video description

Provide computer vision and build systems that rival human sight. Designed for beginners to computer vision or PyTorch.

About This Video

  • Guides you through building state-of-the-art models that are used and developed by industry leaders
  • Provides hands-on experience with quizzes and solutions to give you a deeper understanding of complex vision concepts
  • Use the latest version of PyTorch to develop vision models

In Detail

PyTorch is powerful and simple to use. This course will help you leverage the power of PyTorch to perform image processing. Beginning with an introduction to image processing, the course introduces you to basic deep-learning and optimization concepts. Next, you'll learn to use PyTorch's APIs such as the dynamic graph computation tensor, which can be used for image classification. Starting off with basic 2D images, the course gradually takes you through recognizing more complex images, color, shapes, and more.

Using the Python API, you'll move on to classifying and training your model to identify more complex images—for example, recognizing plant species better than humans. Then you'll delve into AlexNet, ResNet, VGG-net, Generative Adversarial Networks(GANs), neural style transfer, and more–—all by taking advantage of PyTorch's Deep Neural Networks.

Taking this course is your one-stop, hands-on guide to applying computer vision to your projects using PyTorch. You'll create and deploy your own models, and gain the necessary intuition to work on real-world projects.

Please note that a understanding of calculus and linear algebra, along with some experience using Python, are assumed for taking this course.

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Table of contents

  1. Chapter 1 : Getting Started with Computer Vision and PyTorch
    1. The Course Overview 00:04:59
    2. Getting Started with Computer Vision 00:08:11
    3. Installing the Latest Version of PyTorch 00:02:17
  2. Chapter 2 : Dive into Deep Learning with PyTorch
    1. Getting Familiar with Deep Learning 00:07:06
    2. Getting Started with PyTorch Tensor 00:02:31
    3. Building Your First Neural Network 00:06:40
    4. Classifying Handwritten Digits with Your Neural Network 00:04:44
    5. Getting Familiar with Overfitting and Dataset-Splits 00:02:05
  3. Chapter 3 : Working with Cost Functions and Optimizers
    1. Getting to Grips with Cost Functions 00:04:58
    2. Using Modern Optimizers 00:03:28
    3. Getting Familiar with Sequential API 00:02:13
    4. Working with Functional API 00:02:15
    5. Add Adam Optimizer to Handwritten Digit Recognizer 00:01:57
  4. Chapter 4 : Getting Started with Convolutions
    1. Introduction to Convolution and Weight Sharing 00:06:10
    2. Using the Padding, Stride, Filter Size, and Channels 00:05:06
    3. Convolutional Layers and Dimension Matching 00:05:16
    4. Building Your First Convolutional Neural Network 00:03:44
    5. Comparing Performance with Multi-Layer Perceptrons 00:02:04
  5. Chapter 5 : Deep Learning Building Blocks
    1. Introduction to Pooling 00:03:06
    2. Getting to Grips with Dropout 00:03:24
    3. Introduction to Batch Norm 00:05:21
    4. Understanding Residual Networks 00:06:23
    5. Implementing Residual Networks from Scratch 00:04:27
  6. Chapter 6 : Loading and Manipulating Data with PyTorch
    1. Introduction to TorchVision 00:04:17
    2. Taking Advantage of Data Augmentation 00:05:54
    3. Loading Video Datasets 00:03:40
    4. Creating Your Own Dataset 00:05:43
    5. Loading and Saving Models for Transfer Learning 00:06:30
  7. Chapter 7 : Neural Style Transfer
    1. Introduction to Neural Style Transfer 00:05:36
    2. Understanding the Concept of Gram Matrix and Loss Function 00:06:18
    3. Stylize an Image Using Neural Style Transfer 00:09:02
    4. Exploring Other Neural Style Transfer Techniques 00:02:43
  8. Chapter 8 : Generative Adversarial Networks
    1. Introduction to Generative Adversarial Networks 00:05:14
    2. Understanding Deep Convolutional GANs 00:02:51
    3. Implementing the Generator and Discriminator 00:06:25
    4. Implementing the Training Procedure for GANs 00:09:43
    5. Improving Training of GANs 00:04:58

Product information

  • Title: Hands-On Computer Vision with PyTorch 1.x
  • Author(s): Colibri Ltd
  • Release date: March 2020
  • Publisher(s): Packt Publishing
  • ISBN: 9781789614077