English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 03m | 531 MB
Build and interact with your intelligent chatbot with NLP!
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!
This hands-on course covers all the important aspects of Natural Language Processing and Deep Learning models with TensorFlow. Throughout the course, you’ll build an intelligent chatbot with step-by-step instructions to implement them following the intuition topics.
What You Will Learn
- Get grips on various NLP techniques while building an intelligent Chatbot using TensorFlow to help you transform online businesses
- A solid understanding of natural language processing intuitions and its functioning
- Optimize performance and efficiency by implementing end to end deep learning models
- Working on sequence-to-sequence models (used in translation) that reads one sequence and produces another
- Using bag-of-words model as a way of representing textual data
- Build a recurrent neural network for language modeling
Table of Contents
01 The Course Overview
02 Types of Natural Language Processing
03 End-to-End Deep Learning and Bag- of-Words Models
04 Recurrent Neural Networks
05 Build Your Seq2Seq Model and Train It
06 Beam Search Decoding and Attention Mechanisms
07 Installing the TensorFlow Environment
08 Import Dataset and Create Dictionaries and Lists
09 Clean Texts for Questions and Answers
10 Filter Q and A and Create Three Dictionaries for Mapping
11 Add Tokens to Dictionaries and Create an Inverse Dictionary
12 Translating and Sorting All Questions and Answers
13 Create Placeholders for Inputs and Targets
14 Create Encoder RNN and Decode Training Set
15 Decoding the Training and Test_Validation Sets
16 Creating Decoder RNN
17 Building the Seq2Seq Model
18 Setting the Hyperparameters and Defining a Session
19 Getting Training and Test Predictions
20 Loss Error, Optimizer, and Gradient Clipping
21 Padding the Sequence
22 Training Our Chatbot
23 Load the Weights and Run the Session
24 Convert Questions from Strings to Integers
25 Set Up the Chatbot
26 Let’s Chat with Our Chatbot
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