Pytorch Computer Vision Cookbook: Over 70 Recipes To Solve Computer Vision And Image Processing Problems Using Pytorch 1.x
by Michael Avendi /
2020 / English / PDF
37.8 MB Download
Discover powerful ways to explore deep learning algorithms and solve real-world computer vision problems using PythonKey Features
-Solve the trickiest of problems in CV by combining the power of deep learning and neural networks
-Get the most out of PyTorch 1.x capabilities to perform image classification, object detection, and much more
-Train and deploy enterprise-grade, deep learning models for computer vision applications
Book Description
Developers can gain a high-level understanding of digital images and videos using computer vision techniques. With this book, you'll learn how to solve the trickiest of problems in computer vision (CV) using the power of deep learning algorithms, and leverage the latest features of PyTorch 1.x to perform a variety of computer vision tasks.
Starting with a quick overview of the PyTorch library and key deep learning concepts, the book covers common and not-so-common challenges faced while performing image recognition, image segmentation, captioning, image generation, and many other tasks. You'll implement these tasks using various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long-short term memory (LSTM), and generative adversarial networks (GANs). Using a problem-solution approach, you'll solve any issue you might face while fine-tuning the performance of the model or integrating the model into your application. Additionally, you'll even get to grips with scaling the model to handle larger workloads and implement best practices for training models efficiently.
By the end of this book, you'll be able to solve any problem relating to training effective computer vision models.What you will learn
-Implement a multi-class image classification network using PyTorch
-Understand how to fine-tune and change hyperparameters to train deep learning algorithms
-Perform various CV tasks such as classification, detection, and segmentation
-Implement a neural-style transfer network based on CNN and pre-trained models
-Generate new images using generative adversarial networks
-Implement video classification models based on RNN and LSTM
-Discover best practices for training and deploying deep learning algorithms for CV applications
Who This Book Is For
Computer vision professionals, data scientists, deep learning engineers, and AI developers looking for quick solutions for various computer vision problems will find this book useful. Intermediate knowledge of computer vision concepts along with Python programming experience is required.
