Advanced Deep Learning with TensorFlow 2 and Keras : Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more / Rowel Atienza
Material type: TextPublication details: India Packt Publishing 2020Edition: 2nd edDescription: 491 pISBN:- 9781838821654
- 005.133 ATI-R
Item type | Current library | Collection | Shelving location | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|---|
Books | BITS Pilani Hyderabad | 003-007 | General Stack (For lending) | 005.133 ATI-R (Browse shelf(Opens below)) | Available | 42186 |
Book Description
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 is 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.
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