LEADER 00000cam a2200421Ma 4500 
003    OCoLC 
005    20240129213017.0 
006    m     o  d         
007    cr cn||||||||| 
008    061220s2020    xx     go     000 0 eng d 
020    1839213477|q(paperback) 
020    9781839213472|q(paperback) 
035    (OCoLC)1235779331 
040    TOH|beng|cTOH|dOCLCQ|dOCLCO|dOCLCQ|dOCLCL 
049    INap 
082 04 006.3/2 
082 04 006.3/2|qOCoLC|223/eng/20230216 
099    eBook O'Reilly for Public Libraries 
100 1  Ayyadevara, V,|eauthor. 
245 10 Modern Computer Vision with PyTorch /|cAyyadevara, V.
       |h[O'Reilly electronic resource] 
250    1st edition. 
264  1 |bPackt Publishing,|c2020. 
300    1 online resource (824 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file 
365    |b49.99 
520    Get to grips with deep learning techniques for building 
       image processing applications using PyTorch with the help 
       of code notebooks and test questions Key Features 
       Implement solutions to 50 real-world computer vision 
       applications using PyTorch Understand the theory and 
       working mechanisms of neural network architectures and 
       their implementation Discover best practices using a 
       custom library created especially for this book Book 
       Description Deep learning is the driving force behind many
       recent advances in various computer vision (CV) 
       applications. This book takes a hands-on approach to help 
       you to solve over 50 CV problems using PyTorch1.x on real-
       world datasets. You'll start by building a neural network 
       (NN) from scratch using NumPy and PyTorch and discover 
       best practices for tweaking its hyperparameters. You'll 
       then perform image classification using convolutional 
       neural networks and transfer learning and understand how 
       they work. As you progress, you'll implement multiple use 
       cases of 2D and 3D multi-object detection, segmentation, 
       human-pose-estimation by learning about the R-CNN family, 
       SSD, YOLO, U-Net architectures, and the Detectron2 
       platform. The book will also guide you in performing 
       facial expression swapping, generating new faces, and 
       manipulating facial expressions as you explore 
       autoencoders and modern generative adversarial networks. 
       You'll learn how to combine CV with NLP techniques, such 
       as LSTM and transformer, and RL techniques, such as Deep Q
       -learning, to implement OCR, image captioning, object 
       detection, and a self-driving car agent. Finally, you'll 
       move your NN model to production on the AWS Cloud. By the 
       end of this book, you'll be able to leverage modern NN 
       architectures to solve over 50 real-world CV problems 
       confidently. What you will learn Train a NN from scratch 
       with NumPy and PyTorch Implement 2D and 3D multi-object 
       detection and segmentation Generate digits and DeepFakes 
       with autoencoders and advanced GANs Manipulate images 
       using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN Combine 
       CV with NLP to perform OCR, image captioning, and object 
       detection Combine CV with reinforcement learning to build 
       agents that play pong and self-drive a car Deploy a deep 
       learning model on the AWS server using FastAPI and Docker 
       Implement over 35 NN architectures and common OpenCV 
       utilities Who this book is for This book is for beginners 
       to PyTorch and intermediate-level machine learning 
       practitioners who are looking to get well-versed wit... 
542    |fCopyright © 2020 Packt Publishing|g2020 
550    Made available through: Safari, an O'Reilly Media Company.
588 0  Online resource; Title from title page (viewed November 27,
       2020). 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
700 1  Reddy, Yeshwanth,|eauthor. 
710 2  O'Reilly for Higher Education (Firm),|edistributor. 
710 2  Safari, an O'Reilly Media Company. 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781839213472/?ar
       |aAvailable on O'Reilly for Public Libraries 
994    92|bJFN