Book details

  • Publisher: Packt Publishing (April 9, 2020)
  • Format: 77 MB
  • Size: epub,mobi,pdf
  • Publication Date: April 9, 2020
  • Sold by: Amazon.com Services LLC
  • Language: English
  • ASIN: B0862CX2ZL

Discover powerful ways to explore deep learning algorithms and solve real-world computer vision problems using Python

Key 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.

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