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Programming PyTorch for deep learning : creating and deploying deep learning applications

By: Pointer, Ian.
Publisher: Mumbai : Shroff Publishers and Distributors, 2022Description: xv, 200 p. ; ill., (b & w), 24 cm.ISBN: 9789352139095.Subject(s): Artificial intelligence | Machine learning | Python | Computer program language | Adversarial samples | Argmax ()function | Convolutional neural networks (CNNs) | CrossEntropy Loss() function | Environmental sound classification(ESC) | Frequency domain | Generative adversarial networks (GANs) | Image classification | Jupyter Notebook | Kubernetes(K8s) | Memory (LSTM) Networks | Pretrained models | ResNet architecture | Transfer learning | ULMFITDDC classification: 006.32 Summary: Take the next steps toward mastering deep learning, the machine learning method that's transforming the world around us by the second. In this practical book, you'll get up to speed on key ideas using Facebook's open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.
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Books 006.32 POI (Browse shelf) Available 033497

Includes bibliographical references and index.

Take the next steps toward mastering deep learning, the machine learning method that's transforming the world around us by the second. In this practical book, you'll get up to speed on key ideas using Facebook's open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.

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