Home > Store

Deep Learning for Natural Language Processing LiveLessons (Video Training), 2nd Edition

Deep Learning for Natural Language Processing LiveLessons (Video Training), 2nd Edition

Your browser doesn't support playback of this video. Please download the file to view it.

Online Video

Register your product to gain access to bonus material or receive a coupon.

Description

  • Copyright 2020
  • Edition: 2nd
  • Online Video
  • ISBN-10: 0-13-662004-3
  • ISBN-13: 978-0-13-662004-4

Nearly 5 Hours of Video Instruction

An intuitive introduction to processing natural language data with TensorFlow-Keras deep learning models.

Overview

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.
Skill Level

  • Intermediate

Learn How To
  • Preprocess natural language data for use in machine learning applications
  • Transform natural language into numerical representations with word2vec
  • Make predictions with Deep Learning models trained on natural language
  • Apply state-of-the-art NLP approaches with Keras, the high-level API for TensorFlow 2
  • Improve Deep Learning model performance by selecting appropriate model architectures and tuning model hyperparameters

Who Should Take This Course

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.

Course Requirements

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.

Lesson Descriptions

Lesson 1: The Power and Elegance of Deep Learning for NLP
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.

Lesson 2: Word Vectors
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.

Lesson 3: Modeling Natural Language Data
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.

Lesson 4: Recurrent Neural Networks
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.

Lesson 5: Advanced Models
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.

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video training at http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Sample Content

Table of Contents

Introduction
Lesson 1: The Power and Elegance of Deep Learning for NLP
Topics
1.1 Introduction to Deep Learning for Natural Language Processing
1.2 Running the Hands-On Code Examples in Jupyter Notebooks
1.3 Review of Prerequisite Deep Learning Theory
1.4 A Sneak Peek

Lesson 2: Word Vectors
Topics
2.1 Computational Representations of Natural Language Elements
2.2 Visualizing Word Vectors with word2viz
2.3 Localist Versus Distributed Representations
2.4 Elements of Natural Human Language
2.5 The word2vec Algorithm
2.6 Creating Word Vectors with word2vec
2.7 Pre-Trained Word Vectors and doc2vec

Lesson 3: Modeling Natural Language Data
Topics
3.1 Best Practices for Preprocessing Natural Language Data
3.2 The Area Under the ROC Curve
3.4 Document Classification with a Dense Neural Net
3.5 Classification with a Convolutional Neural Net

Lessons 4: Recurrent Neural Networks
Topics
4.1 Essential Theory of RNNs
4.2 RNNs in Practice
4.3 Essential Theory of LSTMs and GRUs
4.4 LSTMs and GRUs in Practice

Lesson 5: Advanced Models
Topics
5.1 Bi-Directional LSTMs
5.2 Stacked LSTMs
5.3 Datasets for NLP
5.4 Sequence Generation
5.5 seq2seq and Attention
5.6 Transfer Learning in NLP: BERT, ELMo, GPT-2 and Other Characters
5.7 Non-Sequential Architectures: The Keras Functional API
5.8 (Financial) Time Series Applications
     
Summary 

Updates

Submit Errata

More Information

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020