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Advanced Deep Learning with TensorFlow 2 and Keras

Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more

Paperback Engels 2020 9781838821654
Verkooppositie 2634
Verwachte levertijd ongeveer 12 werkdagen

Samenvatting

'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.

Specificaties

ISBN13:9781838821654
Taal:Engels
Bindwijze:paperback
Aantal pagina's:512
Druk:2
Verschijningsdatum:28-2-2020
Hoofdrubriek:IT-management / ICT

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Over Rowel Atienza

Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence and received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution in the field of active gaze tracking for human-robot interaction. His current research work focuses on AI and computer vision.

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Inhoudsopgave

1 Introducing Advanced Deep Learning with Keras
2 Deep Neural Networks
3 Autoencoders
4 Generative Adversarial Networks (GANs)
5 Improved GANs
6 Disentangled Representation GANs
7 Cross-Domain GANs
8 Variational Autoencoders (VAEs)
9 Deep Reinforcement Learning
10 Policy Gradient Methods
11 Object Detection
12 Semantic Segmentation
13 Unsupervised Learning Using Mutual Information
14 Other Books You May Enjoy

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        Advanced Deep Learning with TensorFlow 2 and Keras