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Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
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TensorFlow 2.0 Computer Vision Cookbook - Implement machine learning solutions to overcome various computer vision challenges (Paperback)
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TensorFlow 2.0 Computer Vision Cookbook - Implement machine learning solutions to overcome various computer vision challenges (Paperback)
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Get well versed with state-of-the-art techniques to tailor training
processes and boost the performance of computer vision models using
machine learning and deep learning techniques Key Features Develop,
train, and use deep learning algorithms for computer vision tasks
using TensorFlow 2.x Discover practical recipes to overcome various
challenges faced while building computer vision models Enable
machines to gain a human level understanding to recognize and
analyze digital images and videos Book DescriptionComputer vision
is a scientific field that enables machines to identify and process
digital images and videos. This book focuses on independent recipes
to help you perform various computer vision tasks using TensorFlow.
The book begins by taking you through the basics of deep learning
for computer vision, along with covering TensorFlow 2.x's key
features, such as the Keras and tf.data.Dataset APIs. You'll then
learn about the ins and outs of common computer vision tasks, such
as image classification, transfer learning, image enhancing and
styling, and object detection. The book also covers autoencoders in
domains such as inverse image search indexes and image denoising,
while offering insights into various architectures used in the
recipes, such as convolutional neural networks (CNNs), region-based
CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO). Moving on,
you'll discover tips and tricks to solve any problems faced while
building various computer vision applications. Finally, you'll
delve into more advanced topics such as Generative Adversarial
Networks (GANs), video processing, and AutoML, concluding with a
section focused on techniques to help you boost the performance of
your networks. By the end of this TensorFlow book, you'll be able
to confidently tackle a wide range of computer vision problems
using TensorFlow 2.x. What you will learn Understand how to detect
objects using state-of-the-art models such as YOLOv3 Use AutoML to
predict gender and age from images Segment images using different
approaches such as FCNs and generative models Learn how to improve
your network's performance using rank-N accuracy, label smoothing,
and test time augmentation Enable machines to recognize people's
emotions in videos and real-time streams Access and reuse advanced
TensorFlow Hub models to perform image classification and object
detection Generate captions for images using CNNs and RNNs Who this
book is forThis book is for computer vision developers and
engineers, as well as deep learning practitioners looking for go-to
solutions to various problems that commonly arise in computer
vision. You will discover how to employ modern machine learning
(ML) techniques and deep learning architectures to perform a
plethora of computer vision tasks. Basic knowledge of Python
programming and computer vision is required.
General
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