Articles by "PyTorch"

Showing posts with label PyTorch. Show all posts

Ian Pointer ... 220 pages - Publisher: O'Reilly Media; (October, 2019) ... Language: English - ISBN-10: 1492045357 - ISBN-13: 978-1492045359.

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.

Learn how to deploy deep learning models to production + Explore PyTorch use cases from several leading companies + Learn how to apply transfer learning to images + Apply cutting-edge NLP techniques using a model trained on Wikipedia + Use PyTorch’s torchaudio library to classify audio data with a convolutional-based model + Debug PyTorch models using TensorBoard and flame graphs + Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud.

Delip Rao, Brian McMahan ... 256 pages - Publisher: O'Reilly Media; (February, 2019) ... Language: English - ISBN-10: 1491978236 - ISBN-13: 978-1491978238.

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations.

Explore computational graphs and the supervised learning paradigm + Master the basics of the PyTorch optimized tensor manipulation library + Get an overview of traditional NLP concepts and methods + Learn the basic ideas involved in building neural networks + Use embeddings to represent words, sentences, documents, and other features + Explore sequence prediction and generate sequence-to-sequence models + Learn design patterns for building production NLP systems.

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