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He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Programming PyTorch for Deep Learning Creating and Deploying Deep Learning Applications.
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ISBN-13: 9789811367939, 978-9811367939. Advances in Deep Learning. This book introduces readers to both basic and advanced concepts in deep network models. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications.
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From the Back Cover This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. Youll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general.
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Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep.
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ISBN-13: 9783030145224, 978-3030145224. Theory and Applications. Publisher: Springer Nature Switzerland AG, Switzerland.
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