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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
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
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From files

As seen in a previous example, datasets can iterate over files using the .list_files() static method. This method creates a dataset of string tensors, each containing the path of one of the listed files. Each file can then be opened using, for instance, tf.io.read_file() (tf.io contains file-related operations).

The tf.data API also provides some specific datasets to iterate over binary or text files. tf.data.TextLineDataset() can be used to read documents line by line (useful for some public datasets that are listing their image files and/or labels in text files); tf.data.experimental.CsvDataset() can parse CSV files and return their content line by line too.

tf.data.experimental does not ensure the same backward compatibility as other modules. By the time this book reaches our readers, methods may have been moved to tf.data.Dataset or simply removed (for methods that are temporary solutions to some TensorFlow limitations). We invite our readers to check the documentation...
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