Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Rowel Atienza
Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Rowel Atienza

Overview of this book

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (16 chapters)
14
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15
Index

Object Detection

Object detection is one of the most important applications of computer vision. Object detection is the task of simultaneous localization and identification of an object that is present in an image. For autonomous vehicles to safely navigate the streets, the algorithm must detect the presence of pedestrians, roads, vehicles, traffic lights, signs, and unexpected obstacles. In security, the presence of an intruder can be used to trigger an alarm or inform the appropriate authorities.

Though important, object detection has been a long-standing problem in computer vision. Many algorithms have been proposed but are generally slow, with low precision and recall. Similar to what AlexNet [1] has achieved in the ImageNet large-scale image classification problem, deep learning has significantly advanced the area of object detection. State-of-the-art object detection methods can now run in real time and have a much higher precision and recall.

In this chapter, we...