by Pietro Marinelli | Released March 2019 | ISBN: 9781789952339

Build NLP projects to enhance your portfolio.About This VideoYou will learn how to extract the best from you text data to get powerful insights to deliver to Top Management to help them take important decisions.This will be a step by step course. We will build a python notebook that you can reuse and adapt to your problems.We will use the latest version of Tensorflow 2.0 along with latest python packages.In DetailNatural Language Processing (NLP) is the field of Artificial Intelligence that deals with text analysis and understanding. Some of the fields in which NLP is widely used are sentiment classification, spam detection and topic detection. Deep Learning is one of the tools that helps us solving NLP problems.This course will get you started with real world NLP projects and you will learn how to get the best from text data. We will be building and training models in real-world projects and focus on interactions between computers and humans with Tensorflow 2.0. Together we will dive deep in a collection of text, writing a jupyter notebook step by step until we obtain actionable insights and powerful visualizations.By the end of the course, you will be able to build and implement your own NLP techniques and projects effectively with much ease confidently.All the code and supporting files for this course are available on GitHub at:https://github.com/PacktPublishing/Advanced-NLP-Projects-with-TensorFlow-2.0Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : Building a Language Recognizer
   • The Course Overview 00:01:58
   • Data Preparation – Text Processing 00:05:09
   • Build and Train a Neural Network 00:05:01
   • Predict and Evaluate the Outcome 00:03:11
2. Chapter 2 : Word Clustering
   • Data Preparation 00:02:00
   • Create Embedding Space 00:04:07
   • Visualize Words with TensorBoard 00:06:21
3. Chapter 3 : Build a Machine Translation System
   • Introduction to the Problem and Recurrent Neural Networks 00:01:34
   • Model Architecture and Main Ideas Behind It 00:03:00
   • Build and Train the Seq2Seq Model 00:13:28
4. Chapter 4 : Fake News Classifier
   • Introduction 00:01:52
   • Create Word Embedding Combining Different Text Sources 00:09:07
   • Evaluate the Model with Different Combination of Text Sources 00:03:31
5. Chapter 5 : Build a Text Summarization System
   • Introduction- Text Summarization System 00:02:19
   • Explore and Process the Data 00:02:43
   • Build and Train the Neural Network 00:05:36
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