Build Chatbots! Chatbots and Conversational UI Development With Neural Networks

Build Chatbots! Chatbots and Conversational UI Development With Neural Networks

Conversation as an interface is the best way for machines to interact with us using the universally accepted human tool that is language. Chatbots and voice user interfaces are two flavors of conversational UIs. Chatbots are real-time, data-driven answer engines that talk in natural language and are context-aware. Voice user interfaces are driven by voice and can understand and respond to users using speech.

Chatbots and Conversational UI Development with Neural Networks is a fast-paced, hand-on course that explores both types of conversational UIs by leveraging APIs from multiple platforms. We'll take a project-based approach to understand how these UIs are built and the best use cases for deploying them. We'll start by building a simple messaging bot from the Facebook Messenger API to understand the basics of bot building. Then we move on to creating a Task model that can perform complex tasks such as ordering and planning events with the newly-acquired-by-Google Dialogflow and Microsoft Bot framework. We then turn to voice-enabled UIs that are capable of interacting with users using speech with Amazon Alexa and Google Home.

Learning Objectives

Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern applied AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will

• Understand the different use cases of Conversational UIs with this project-based guide
• Build feature-rich Chatbots and deploy them on multiple platforms
• Get real-world examples of voice-enabled UIs for personal and home assistance.
• Cover advanced and state-of-the-art neural network architectures
• Understand the theory and math behind neural networks
• Train DNNs and apply them to modern deep learning problems
• Use CNNs for object detection and image segmentation
• Implement generative adversarial networks (GANs) and variational autoencoders to generate new images
• Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models
• Understand DL techniques, such as meta-learning and graph neural networks