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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

The internet – the new El Dorado of data science

The internet was not only a revolution in communication; it also deeply transformed data science. It became much easier for scientists to share images and content by uploading them online, leading to the creation of public datasets for experimentation and benchmarking. Moreover, not only researchers but soon everyone, all over the world, started adding new content online, sharing images, videos, and more at an exponential rate. This started big data and the golden age of data science, with the internet as the new El Dorado.

By simply indexing the content that is constantly published online, image and video datasets reached sizes that were never imagined before, from Caltech-101 (10,000 images, published in 2003 by Li Fei-Fei et al., Elsevier) to ImageNet (14+ million images, published in 2009 by Jia Deng et al., IEEE) or Youtube-8M (8+ million videos, published in 2016 by Sami Abu-El-Haija et al., including Google). Even companies...

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