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Building Natural Language Applications with TensorFlow

Video Description

Build and interact with your intelligent chatbot with NLP!

About This Video

  • Master Google's TensorFlow framework by building an intelligent chatbot using Natural Language Processing and Deep Learning model
  • Get complete exposure to all the important aspects of Natural Language Processing and Deep Learning models with TensorFlow
  • This course is designed with minimal theory and maximal practical implementation, followed by step-by-step instructions to get you up-and-running

In Detail

Do you want your machine to analyze, understand, and generate human speech? Do you want to build chatbots? NLP is the next step in bridging many concerns that users, businesses, and developers experience with customer service.

Chatbots are making it easier and replacing humans everywhere in social media, websites, stores, and even business-to-business conversations. With a less talk and more action approach, this course will lead you through various implementations of NLP techniques by implementing end-to-end deep learning models and creating an intelligent chatbot on our own.

Get your hands on this course to learn the most fascinating technology in the field of AI and leverage the power of TensorFlow right away!

The codes of this course can be found on GitHub: https://github.com/PacktPublishing/-Building-Natural-Language-Applications-with-TensorFlow

Table of Contents

  1. Chapter 1 : Getting Started with NLP and Deep Learning Intuitions
    1. The Course Overview 00:04:38
    2. Types of Natural Language Processing 00:05:17
    3. End-to-End Deep Learning and Bag- of-Words Models 00:09:17
    4. Recurrent Neural Networks 00:07:59
    5. Build Your Seq2Seq Model and Train It 00:05:49
    6. Beam Search Decoding and Attention Mechanisms 00:05:41
  2. Chapter 2 : Transform Raw Data into Machine-Understandable Format
    1. Installing the TensorFlow Environment 00:05:45
    2. Import Dataset and Create Dictionaries and Lists 00:11:19
    3. Clean Texts for Questions and Answers 00:04:07
    4. Filter Q and A and Create Three Dictionaries for Mapping 00:03:51
    5. Add Tokens to Dictionaries and Create an Inverse Dictionary 00:03:16
    6. Translating and Sorting All Questions and Answers 00:05:20
  3. Chapter 3 : Build a Seq2Seq Model with Encoder and Decoder RNN
    1. Create Placeholders for Inputs and Targets 00:04:27
    2. Create Encoder RNN and Decode Training Set 00:05:26
    3. Decoding the Training and Test/Validation Sets 00:03:34
    4. Creating Decoder RNN 00:04:23
    5. Building the Seq2Seq Model 00:03:48
  4. Chapter 4 : Train a Model with Hyperparameter Tuning
    1. Setting the Hyperparameters and Defining a Session 00:03:56
    2. Getting Training and Test Predictions 00:01:20
    3. Loss Error, Optimizer, and Gradient Clipping 00:02:32
    4. Padding the Sequence 00:04:58
    5. Training Our Chatbot 00:08:06
  5. Chapter 5 : Let's Test Our Intelligent Chatbot
    1. Load the Weights and Run the Session 00:01:32
    2. Convert Questions from Strings to Integers 00:01:09
    3. Set Up the Chatbot 00:02:56
    4. Let's Chat with Our Chatbot 00:02:47