Description

This comprehensive course will help you learn the basics to advanced mechanisms of chatbot development using deep learning with Python. This course is a complete package for beginners to learn chatbot fundamentals with deep learning and its applications and build it from scratch using deep learning (RNNs) with Python.

AI-powered chatbots are also capable of automating various tasks, including sales and marketing, customer service, and administrative and operational tasks. In this course about developing advanced chatbots with deep learning, we will understand their applications and build from scratch using deep learning with Python. The course begins with a brief overview and the fundamentals of deep learning for chatbots. We will understand and compare conventional chatbots with deep learning-based chatbots. Then, we will explore self-learning chatbots, including generative chatbots and retrieval chatbots. You will learn more about deep learning-empowered chatbot features and compare and distinguish the abilities of conventional chatbots and self-learning chatbots in real action. We will focus on chatbot development with deep learning, tokenization, setting up an Encoder-Decoder, implementing RNN-based model development, and finally, training, testing, and validating the chatbot we developed. Upon completing this course successfully, you will relate concepts and understand theories of chatbots in various domains, understand and implement deep learning models for building real-world chatbots, and evaluate deep learning-based chatbot models. All resources are available at: https://github.com/PacktPublishing/Advanced-Chatbots-with-Deep-Learning-Python

What You Will Learn

Relate the concepts and theories for chatbots in various domains
Compare conventional chatbots with deep learning-based chatbots
Understand deep learning algorithms for chatbots
Implement deep learning models for building real-world chatbots
Learn about tokenization and setting up an encoder-decoder
Implement recurrent neural network-based model development

Audience

This course is designed for individuals looking to advance their skills in applied deep learning, acquire knowledge regarding the relationships of data analysis with deep learning, wish to build customized chatbots for their applications, learn to implement deep learning algorithms for chatbots, and are passionate about rule-based and self-learning chatbots. Deep learning practitioners/scholars working on chatbot concepts would benefit from this course. No prior knowledge of chatbots, deep learning, data analysis, or mathematics is needed. Basic to intermediate Python knowledge is required.

Approach

The course is crafted to help you understand the impact of chatbots in real-world applications and provides a unique hands-on experience using a project. This course is easily understandable, with engaging content covering necessary theoretical concepts, a practical approach with live coding, and an in-depth project covering the complete course content.

Key Features

Learn basic to advanced mechanisms of developing chatbots using deep learning with Python * Use Python to evaluate datasets based on conversational notes, online resources, and websites * Explore conventional/self-learning chatbots in action and chatbot development with deep learning

Github Repo

https://github.com/PacktPublishing/Advanced-Chatbots-with-Deep-Learning-Python

About the Author

AI Sciences

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

Course Outline

1. Introduction

This section focuses on introducing the author and the AI Sciences group and describes the course content you will be learning.

1. Course and Instructor Introduction

In this video, we will be introduced to the course's author and his qualifications and experience in teaching learners on the online platform.

2. AI Sciences Introduction

This video briefly introduces the AI Sciences group and discusses the benefits of learning through AI Sciences.

3. Course Description

In this lesson, we will get an overview of what is covered in the course, including a comparison of chatbots and the benefits of chatbots. We will look at the chatbots in action. We will also look at deep learning frameworks in chatbots.

2. Fundamentals of Chatbots for Deep Learning

This section focuses on the basics of chatbots, how deep learning benefits chatbots, conventional versus AI chatbots, the use of chatbots in the medical field, and the use of chatbots in business and ecommerce.

1. Module Introduction

This introduction video elaborates on the concepts to be covered in this module, including comparing chatbots, the conventional versus AI chatbots, and use of chatbots in various fields of applications.

2. Conventional Versus AI Chatbots

In this video, we will understand conventional and artificial intelligence-based chatbots and understand the differences between the two kinds.

3. Generative Versus Retrieval Chatbots

In this lecture, you will learn about a generative and retrieval chatbot and compare the differences between the two chatbots.

4. Benefits of Deep Learning Chatbots

In this video, we will look at what deep learning chatbots are and how they work. We will also understand the benefits of deep learning chatbots, including improved customer experience, service integration, customer care, personalized services, and saving resources.

5. Chatbots in Medical Domain

We will explore the first kind of chatbot operating in the medical domain. We will look at the benefits, including the ability to schedule appointments, check patient symptoms, provide support, help with coverage and insurance claims, and improve the patient experience.

6. Chatbots in Business

In this video, we will understand the benefits of chatbots in businesses, including lead qualification, lead nurturing, and data mining techniques.

7. Chatbots in Ecommerce

Let's understand the benefits of chatbots in ecommerce, including personalized services, real-time interaction, collection of feedback, provide metrics, lead generation, deep analytics, and storytelling.

3. Deep Learning-Based Chatbot Architecture and Development

This section focuses on chatbot architecture and development based on deep learning, including understanding deep learning architecture, encoders and decoders, data preparation, importing libraries, and making predictions.

1. Module Introduction

In this video, you will learn more about deep learning and the general architecture, chatbot development with deep learning, and its related concepts.

2. Deep Learning Architecture

This video elaborates on the deep learning architecture in chatbots, integration of deep learning with chatbots, natural language processing (NLP), source content, interaction history, and analytics.

3. Encoder Decoder

In this video, we will understand what encoder and decoder are, their architecture, and long short-term memory (LSTM), a neural network used in deep learning.

4. Steps Involved

In this lesson, you will learn about the various steps involved in developing chatbots based on deep learning techniques. We will install the required packages, define chat models and tokenization, and set up the encoder-decoder model.

5. Project Overview and Packages

After theoretically learning about chatbots, we will now develop a chatbot practically by importing the requisite libraries-TensorFlow NumPy, Keras, and Pickle. We will build a chatbot based on a story and make predictions of true or false.

6. Importing Libraries

This video demonstrates how to import step by step the four main libraries required for our project, including TensorFlow, NumPy, Keras, and Pickle, and you will learn about importing Sequential and Model.

7. Data Preparation

We will use the two datasets available for our project, the training QA and the testing QA. We will save them in our project folder and invoke them as needed.

8. Develop Vocabulary

In this video, you will learn to combine the datasets, test data, and train data; we will use the all_data function to combine the datasets.

9. Max Story and Question Length

In this video, you will learn to define the project's story length and question length, determined by the number of words in the story or question.

10. Tokenizer

You will learn to import the tokenizer with and without filters. We will fit the tokenizer into our vocabulary using the fit_on_texts built-in command.

11. Separation and Sequence

In this video, you will learn to make the train-test story questions and answers. We will then use a for loop to enter the story, question, and answer into the respective parts.

12. Vectorize Stories

In this video, we will understand how to vectorize the story by defining functions, using the data, and developing an index and tokenizer. We will also determine the maximum story length calculated.

13. Vectorizing Train and Test Data

After creating the vectorization function, we will input the stories, queries, and answers using the vectorize function that we created using the train data.

14. Encoding

We will begin to develop our deep learning model and input placeholders to store the maximum story length, question length, and define the vocabulary size. We will now build our encoder using the sequential model.

15. Answer and Response

After learning to create our encoders for the input sequences and the questions, the encoders will match the data and obtain the responses.

16. Model Completion

In this video, after training and testing the data, querying the questions, and obtaining responses, we are now at model completion with compiling the questions and responses to check for accuracy.

17. Predictions

After checking our model for accuracy, we will make predictions of our results from the model we created. We will visualize the predictions using the test data.

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Advanced Chatbots with Deep Learning and Python

By Packt

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