Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face (English Edition)

Price: $29.78
(as of Dec 25,2024 00:39:32 UTC – Details)

ASIN ‏ : ‎ B0CXJBTRN1
Publisher ‏ : ‎ BPB Publications; 1st edition (March 8, 2024)
Publication date ‏ : ‎ March 8, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 4805 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 508 pages

Transformers have become an indispensable tool in natural language processing (NLP), computer vision, and speech processing. In this post, we will explore how to build transformer models using PyTorch 2.0 and Hugging Face.

PyTorch is a popular deep learning framework that provides a flexible and easy-to-use platform for building and training neural networks. With the release of PyTorch 2.0, developers now have access to even more powerful tools for building state-of-the-art models.

Hugging Face is a leading provider of pre-trained transformer models that can be easily integrated with PyTorch. Their models have been fine-tuned on large datasets and can be used for a wide range of tasks, including text classification, named entity recognition, image recognition, and speech recognition.

In this post, we will walk through how to use PyTorch 2.0 and Hugging Face to build transformer models for NLP, computer vision, and speech processing. We will cover topics such as tokenization, model architecture, training, and evaluation, providing you with all the tools you need to get started with transformer models in PyTorch.

Whether you are a seasoned deep learning practitioner or a newcomer to the field, this post will help you harness the power of transformer models for your own projects. So grab your copy of “Building Transformer Models with PyTorch 2.0: NLP, computer vision, and speech processing with PyTorch and Hugging Face” and start building cutting-edge AI applications today!
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