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Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

By : Benjamin Planche, Eliot Andres
3.3 (12)
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Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

3.3 (12)
By: Benjamin Planche, Eliot Andres

Overview of this book

Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.
Table of Contents (16 chapters)
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1
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision
2
Computer Vision and Neural Networks
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5
Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6
Influential Classification Tools
9
Section 3: Advanced Concepts and New Frontiers of Computer Vision
10
Training on Complex and Scarce Datasets

More power than ever

Luckily, since the internet was booming, so was computing power. Hardware kept becoming cheaper as well as faster, seemingly following Moore's famous law (which states that processor speeds should double every two years—this has been true for almost four decades, though a deceleration is now being observed). As computers got faster, they also became better designed for computer vision. And for this, we have to thank video games.

The graphical processing unit (GPU) is a computer component, that is, a chip specifically designed to handle the kind of operations needed to run 3D games. Therefore, a GPU is optimized to generate or manipulate images, parallelizing these heavy matrix operations. Though the first GPUs were conceived in the 80s, they became affordable and popular only with the advent of the new millennium.

In 2007, NVIDIA, one of the main companies designing GPUs, released the first version of CUDA, a programming language that allows developers...

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