Natural Language Processing with Transformers in Python

Transformer 模型是现代 NLP 中的事实标准。它们已经在很大程度上证明了自己是最具表现力、最强大的语言模型，一次又一次地击败了所有主要的基于语言的基准。

在本课程中，我们将学习您开始使用 Google AI 的 BERT 或 Facebook AI 的 DPR 等转换器模型构建尖端性能 NLP 应用程序所需的所有知识。

在所有这些用例中，我们都通过各种示例来确保变压器是什么、如何以及为什么如此重要。除了这些部分，我们还完成了两个完整的 NLP 项目，一个用于金融 Reddit 数据的情感分析，另一个涵盖成熟的开放域问答应用程序。

所有这些都得到了其他几个部分的支持，这些部分鼓励我们学习如何更好地设计、实现和衡量我们模型的性能，例如：

NLP 的历史和 Transformer 的来源

NLP 常用的预处理技术

变压器背后的理论

如何微调变压器

Duration: 11h 23m | Video: .MP4, 1280×720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 3.28 GB
Genre: eLearning | Language: English

Learn next-generation NLP with transformers using PyTorch, TensorFlow, and HuggingFace!

What you’ll learn
How to use transformer models for NLP
Modern natural language processing technologies
An overview of recent development in NLP
Python
Machine Learning
Natural Language Processing
Tensorflow
PyTorch
Transformers
Sentiment Analysis
Question and Answering
Named Entity Recognition

Requirements
Knowledge of Python
Experience with data science a plus
Experience with NLP a plus
Description
Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.

In this course, we learn all you need to know to get started with building cutting-edge performance NLP applications using transformer models like Google AI’s BERT, or Facebook AI’s DPR.

We cover several key NLP frameworks including:

HuggingFace’s Transformers

TensorFlow 2

PyTorch

spaCy

NLTK

Flair

And learn how to apply transformers to some of the most popular NLP use-cases:

Language classification/sentiment analysis

Named entity recognition (NER)

Question and Answering

Similarity/comparative learning

Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.

All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:

History of NLP and where transformers come from

Common preprocessing techniques for NLP

The theory behind transformers

How to fine-tune transformers

We cover all this and more, I look forward to seeing you in the course!

Who this course is for:
Aspiring data scientists and ML engineers interested in NLP
Practitioners looking to upgrade their skills
Developers looking to implement NLP solutions
Data scientist
Machine Learning Engineer
Python Developers