教程演示🔗

Video description视频解说

Nearly 4 Hours of Video Instruction
近4小时的视频教学

An intuitive introduction to processing natural language data with TensorFlow-Keras deep learning models.
直观介绍如何使用 TensorFlow-Keras 深度学习模型处理自然语言数据。

Overview概述

Deep Learning for Natural Language Processing LiveLessons, Second Edition, is an introduction to building natural language models with deep learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow 2, the most popular Deep Learning library. In early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data.
用于自然语言处理的深度学习 LiveLessons（第二版）介绍了如何使用深度学习构建自然语言模型。这些课程通过交互式动手 Jupyter 笔记本演示，将对基本理论的直观解释带入生活。示例包括 Python 和 Keras，这是最流行的深度学习库 TensorFlow 2 的高级 API。在早期的课程中，涵盖了使用自然语言数据的细节，包括如何将自然语言转换为可以通过机器学习方法轻松处理的数字表示。在后面的课程中，将利用最先进的深度学习架构对自然语言数据进行预测。

About the Instructor关于讲师

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He presents a popular series of deep learning tutorials published by Addison-Wesley and is the author of the bestselling book Deep Learning Illustrated. Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy, as well as guest lecturing at Columbia University and New York University. He holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading journals since 2010.
Jon Krohn 是机器学习公司 untapt 的首席数据科学家。他介绍了 Addison-Wesley 出版的一系列流行的深度学习教程，并且是畅销书《深度学习图解》的作者。Jon 在纽约市数据科学学院的课堂上教授他的深度学习课程，并在哥伦比亚大学和纽约大学担任客座讲师。他拥有牛津大学神经科学博士学位，自 2010 年以来一直在领先期刊上发表有关机器学习的文章。


Skill Level技能水平

• Intermediate中间

Learn How To了解如何

• Preprocess natural language data for use in machine learning applications
  预处理自然语言数据以用于机器学习应用程序
• Transform natural language into numerical representations with word2vec
  使用 word2vec 将自然语言转换为数字表示
• Make predictions with Deep Learning models trained on natural language
  使用基于自然语言训练的深度学习模型进行预测
• Apply state-of-the-art NLP approaches with Keras, the high-level API for TensorFlow 2
  使用 Keras（TensorFlow 2 的高级 API）应用最先进的 NLP 方法
• Improve Deep Learning model performance by selecting appropriate model architectures and tuning model hyperparameters
  通过选择适当的模型架构和调整模型超参数来提高深度学习模型性能

Who Should Take This Course
谁应该参加这门课程

These LiveLessons are perfectly suited to software engineers, data scientists, analysts, and statisticians with an interest in applying Deep Learning to natural language data. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful.
这些 LiveLessons 非常适合对将深度学习应用于自然语言数据感兴趣的软件工程师、数据科学家、分析师和统计学家。代码示例是用 Python 提供的，因此熟悉它或其他面向对象的编程语言会有所帮助。

Course Requirements课程要求

The author’s Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons, or familiarity with the topics covered in Chapters 5 through 9 of his book Deep Learning Illustrated, are a prerequisite.
作者使用 TensorFlow、Keras 和 PyTorch LiveLessons 进行深度学习，或者熟悉其著作《深度学习图解》第 5 章至第 9 章中涵盖的主题，是先决条件。

Lesson Descriptions课程描述

Lesson 1: The Power and Elegance of Deep Learning for NLP
第 1 课：深度学习在 NLP 中的强大和优雅
This lesson starts off by examining Natural Language Processing and how it has been revolutionized in recent years by Deep Learning approaches. Next comes a review of how to run the code in these LiveLessons. This is followed by the foundational Deep Learning theory that is essential for building an NLP specialization upon. Finally, the lesson provides you with a sneak peek at the capabilities you’ll develop over the course of all five lessons.
本课首先研究自然语言处理，以及近年来深度学习方法如何彻底改变自然语言处理。接下来是关于如何在这些 LiveLessons 中运行代码的回顾。紧随其后的是基础深度学习理论，该理论对于构建 NLP 专业化至关重要。最后，本课程让您先睹为快，了解您将在所有五节课中培养的能力。

Lesson 2: Word Vectors第 2 课：词向量
The lesson begins with a little linguistics section that introduces computational representations of natural language elements. Then it turns to illustrating what word vectors are as well as how the beautiful word2vec algorithm creates them.
本课程从语言学小部分开始，介绍自然语言元素的计算表示。然后，它转向说明什么是词向量，以及漂亮的 word2vec 算法如何创建它们。

Lesson 3: Modeling Natural Language Data
第 3 课：自然语言数据建模
In the preceding lesson, you learned about vector-space embeddings and creating word vectors with word2vec. That process identified shortcomings of our natural language data, so this lesson begins with coverage of best practices for preprocessing language data. Next, on the whiteboard, Jon works through how to calculate a concise and broadly useful summary metric called the Area Under the Curve of the Receiver Operator Characteristic. You immediately learn how to calculate that summary metric in practice by building and evaluating a dense neural network for classifying documents. The lesson then goes a step further by showing you how to add convolutional layers into your deep neural network as well.
在上一课中，您学习了向量空间嵌入和使用 word2vec 创建词向量。该过程发现了自然语言数据的缺点，因此本课首先介绍了预处理语言数据的最佳实践。接下来，在白板上，Jon 将介绍如何计算一个简洁且广泛有用的摘要指标，称为“接收机操作员特征曲线下面积”。通过构建和评估用于对文档进行分类的密集神经网络，您可以立即学习如何在实践中计算该汇总指标。然后，本课程更进一步，向您展示了如何将卷积层添加到深度神经网络中。

Lesson 4: Recurrent Neural Networks
第 4 课：递归神经网络
This lesson kicks off by delving into the essential theory of Recurrent Neural Networks, a Deep Learning family that’s ideally suited to handling data that occur in a sequence like languages do. You immediately learn how to apply this theory by incorporating an RNN into your document classification model. Jon then provides a high-level theoretical overview of especially powerful RNN variants–the Long Short-Term Memory Unit and the Gated Recurrent Unit–before showing you how to incorporate these variants into your deep learning models as well.
本课首先深入探讨递归神经网络的基本理论，这是一个深度学习系列，非常适合像语言一样处理按顺序出现的数据。通过将 RNN 合并到文档分类模型中，您可以立即学习如何应用此理论。然后，Jon 对特别强大的 RNN 变体（长短期记忆单元和门控循环单元）进行了高层次的理论概述，然后向您展示了如何将这些变体合并到深度学习模型中。

Lesson 5: Advanced Models
第 5 课：高级模型
This lesson expands your natural language modeling capabilities further by examining special cases of the LSTM, namely the Bi-Directional and Stacked varieties. Jon also arms you with a rich set of natural language data sets that you can use to train powerful Deep Learning models. To wrap up these LiveLessons, Jon takes you on a journey through other advanced approaches, including sequence generation, seq2seq models, attention, transfer learning, non-sequential network architectures, and financial time series applications.
本 节 课 通过 研究 LSTM 的 特殊 情况 （即 Bi-Directional 和 Stacked 变体），进一步扩展了您的自然语言建模功能。Jon 还为您提供了一组丰富的自然语言数据集，您可以使用这些数据集来训练强大的深度学习模型。为了结束这些直播课程，Jon 将带您了解其他高级方法，包括序列生成、seq2seq 模型、注意力、迁移学习、非序列网络架构和金融时间序列应用程序。

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关于 Pearson 视频培训

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