| Description |
1 online resource (1 video file (1 hr., 59 min.)) : sound, color. |
| Playing Time |
015900 |
| Description |
digital rdatr |
|
video file rdaft |
| Performer |
AI Sciences, presenter. |
| Note |
"Published in February 2023." |
| Summary |
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. 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. 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. |
| Subject |
Human-computer interaction.
|
|
Machine learning.
|
|
Python (Computer program language)
|
|
Artificial intelligence.
|
|
Apprentissage automatique. |
|
Python (Langage de programmation) |
|
Intelligence artificielle. |
|
artificial intelligence. |
|
Artificial intelligence |
|
Human-computer interaction |
|
Machine learning |
|
Python (Computer program language) |
| Genre |
Instructional films.
|
|
Films de formation.
|
| Added Author |
AI Sciences (Firm), presenter.
|
|
Packt Publishing, publisher.
|
| ISBN |
9781837632534 (electronic video) |
|
1837632537 (electronic video) |
|
More Resources