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Interactive Chatbots with TensorFlow

Video Description

Build chatbots of the future. Create sophisticated conversational agents using NLP and TensorFlow

About This Video

  • Build smart and interactive chatbots using NLP and TensorFlow and use them for business or personal use
  • Train and build smart chatbots that simulate written speech with authentic conversations
  • Integrate chatbots to your business applications to provide solutions 24/7 without enduring long wait periods involving human agents, thus ensuring customer satisfaction

In Detail

Have you ever waited for a long time to get a solution from a customer care executive? Also, wouldn't it be nice to have a personal assistant handy to help you with queries and give suggestions? Chatbots provides solutions to these issues.

This course will show you how to create chatbots based on two models. You will design a user-friendly chatbot which responds using perfect grammar and informative answers from a predefined database. You will also learn how to build chatbots that form the answers themselves without having to look into a database. You will also understand the capabilities, and overcome the limitations, of each chatbot. This helps to reduce the need for human effort and costs. Using a chatbot will help scale your business and improve customer relations.

By the end of the course, you'll be able to build smart chatbots that are always ready to provide solutions.

All the code files for this course are available on Github at - https://github.com/PacktPublishing/Interactive-Chatbots-with-TensorFlow-

Downloading 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.

Table of Contents

  1. Chapter 1 : NLP Practices for Chatbot Development
    1. The Course Overview 00:02:08
    2. Tokenization 00:04:04
    3. Stemming 00:03:20
    4. Lemmatization 00:02:09
    5. Vectorization 00:04:47
    6. Word Embedding 00:03:12
    7. Named Entity Recognition 00:02:33
    8. Text Classification 00:02:32
  2. Chapter 2 : Building a Retrieval-Based Chatbot
    1. Understanding Retrieval-Based Chatbots 00:03:32
    2. Choosing the Ideal Similarity Measures 00:03:40
    3. Preparing Data for Your First Retrieval Chatbot 00:00:59
    4. Building Your First Functional Chatbot 00:03:15
  3. Chapter 3 : Integrating Neural Networks
    1. Implementing Linear Models 00:03:46
    2. Transitioning from Linear Models to Nonlinear Neural Networks 00:01:29
    3. Understanding Overfitting and Regularization 00:06:11
    4. Training Word2vec Models 00:03:47
    5. Deploying Recurrent Neural Networks (RNNs) 00:01:11
    6. Applying LSTM to Named Entity Recognition 00:07:56
    7. Text Classification Using LSTM 00:06:56
  4. Chapter 4 : Generative Chatbots: Chatbots of the future
    1. Understanding Generative Chatbots 00:01:30
    2. Incorporating Sequence2Sequence Models 00:01:45
    3. Preparing Data for the Model 00:04:36
    4. Preprocessing the Data 00:06:14
    5. Building the Encoder 00:05:51
    6. Building the Decoder 00:02:00
    7. Training and Evaluating the Model 00:04:21
    8. Deploying the Chatbot 00:02:10
  5. Chapter 5 : Making Your Chatbot Attentive
    1. Understanding Attention 00:01:49
    2. Attention Mechanisms in Images 00:01:47
    3. Soft versus Hard Attention 00:01:06
    4. Implementing Attention 00:01:44
    5. Adding Attention to Our seq2seq Model 00:01:46
    6. Training and Evaluating the Model 00:06:47
  6. Chapter 6 : Contextual Generative Chatbots
    1. What Is Context? 00:01:36
    2. Comparing States and Transitions 00:02:23
    3. Complete System Overview 00:01:30
    4. Building a Contextually Aware System 00:02:48
    5. Conclusion 00:04:33