Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBER
Rothman, Denis
- 出版商: Packt Publishing
- 出版日期: 2021-01-28
- 售價: $3,630
- 貴賓價: 9.5 折 $3,449
- 語言: 英文
- 頁數: 384
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1800565798
- ISBN-13: 9781800565791
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相關分類:
Python、程式語言、DeepLearning、TensorFlow、Text-mining
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相關翻譯:
深入理解Transformer自然語言處理 (簡中版)
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其他版本:
Transformers for Natural Language Processing : Build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, 2/e (Paperback)
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相關主題
商品描述
Become an AI language understanding expert by mastering the quantum leap of Transformer neural network models
Key Features
- Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models
- Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine
- Learn training tips and alternative language understanding methods to illustrate important key concepts
Book Description
The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers.
The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.
The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.
By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
What You Will Learn
- Use the latest pretrained transformer models
- Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models
- Create language understanding Python programs using concepts that outperform classical deep learning models
- Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP
- Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more
- Measure productivity of key transformers to define their scope, potential, and limits, in production
Who this book is for
Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers.
Readers who can benefit the most from this book include deep learning & NLP practitioners, data analysts and data scientists who want an introduction to AI language understanding to process the increasing amounts of language-driven functions.
商品描述(中文翻譯)
成為 AI 語言理解專家,掌握 Transformer 神經網絡模型的量子飛躍
主要特點
- 建立和實施最先進的語言模型,如原始 Transformer、BERT、T5 和 GPT-2,使用超越傳統深度學習模型的概念
- 使用 Google Colaboratory Notebooks 進行 Python 的實作應用,無需在本地機器上安裝任何東西
- 學習訓練技巧和替代的語言理解方法,以說明重要的關鍵概念
書籍描述
Transformer 架構已被證明在超越當前使用的傳統 RNN 和 CNN 模型方面具有革命性。透過應用即學的方式,《自然語言處理中的 Transformers》詳細探討了機器翻譯、語音轉文字、文字轉語音、語言建模、問答系統以及許多其他 NLP 領域的深度學習。
本書帶您通過 Python 進入 NLP,並檢視由 Google、Facebook、Microsoft、OpenAI 和 Hugging Face 等先驅創建的 Transformer 架構中的各種著名模型和數據集。
本書分為三個階段進行訓練。第一階段介紹 Transformer 架構,從原始 Transformer 開始,然後轉向 RoBERTa、BERT 和 DistilBERT 模型。您將發現一些小型 Transformer 的訓練方法,這些方法在某些情況下可以超越 GPT-3。在第二階段,您將應用 Transformer 於自然語言理解 (NLU) 和自然語言生成 (NLG)。最後,第三階段將幫助您掌握高級語言理解技術,如優化社交網絡數據集和假新聞識別。
在本書結束時,您將從認知科學的角度理解 Transformer,並能熟練應用科技巨頭的預訓練 Transformer 模型於各種數據集。
您將學到的內容
- 使用最新的預訓練 Transformer 模型
- 理解原始 Transformer、GPT-2、BERT、T5 和其他 Transformer 模型的運作
- 使用超越傳統深度學習模型的概念創建語言理解的 Python 程式
- 使用各種 NLP 平台,包括 Hugging Face、Trax 和 AllenNLP
- 將 Python、TensorFlow 和 Keras 程式應用於情感分析、文本摘要、語音識別、機器翻譯等
- 測量關鍵 Transformer 的生產力,以定義其範圍、潛力和限制
本書適合對象
由於本書不教授基本編程,您必須熟悉神經網絡、Python、PyTorch 和 TensorFlow,以便學習如何使用 Transformers 實現它們。
最能從本書中受益的讀者包括深度學習和 NLP 實踐者、數據分析師和數據科學家,他們希望了解 AI 語言理解,以處理日益增加的語言驅動功能。