STEM DLI Workshop
Science, Technology, Engineering, and Math
As our world continues to evolve and become more digital, conversational AI is increasingly used to facilitate human-to-machine communication. Conversational AI is the technology that powers automated messaging and speech-enabled applications, and its applications are used in various industries to improve overall customer experience, while improving customer service efficiency. Conversational AI pipelines are complex and expensive to develop from scratch. In this course, you'll learn how to build a conversational AI service using the NVIDIA Riva framework. Riva provides a complete, GPU-accelerated software stack, making it easy for developers to quickly create, deploy, and run end-to-end, real-time conversational AI applications that can understand terminology that’s unique to each company and its customers. The Riva framework includes pretrained conversational AI models, tools, and optimized services for speech, vision, and natural language understanding (NLU) tasks. With Riva, developers can create customized language-based AI services for intelligent virtual assistants, virtual customer service agents, real-time transcription, multi-user diarization, chatbots, and much more. In this workshop, you’ll learn how to quickly build and deploy production quality conversational AI applications with real-time transcription and natural language processing (NLP) capabilities. You’ll integrate NVIDIA Riva automatic speech recognition (ASR) and named entity recognition (NER) models with a web-based application to produce transcriptions of audio inputs with highlighted relevant text. You'll then customize the NER model, using NVIDIA TAO Toolkit to provide different targeted highlights for the application. Finally, you'll explore the production-level deployment performance and scaling considerations of Riva services with Helm Charts and Kubernetes clusters.
This course is only available to students and faculties, please register with .edu email or provide your student/relevent ID.
By participating in this workshop, you’ll learn:
Duration: 8 hours
Prerequisites: Basic Python programming experience Fundamental understanding of a deep learning framework, such as TensorFlow, PyTorch, or Keras Basic understanding of neural networks.
Technologies: NVIDIA Riva, NVIDIA TAO Toolkit, Kubernetes
Assessment Type: Skills-based coding assessments evaluate your ability to build a conversational AI application Multiple-choice questions evaluate your understanding of the conversational AI concepts presented in the class.
Certificate: Upon successful completion of the assessment, you’ll receive an NVIDIA DLI certificate to recognize your subject matter competency and support your professional career growth.
Hardware Requirements: You’ll need a desktop or laptop computer capable of running the latest version of Chrome or Firefox. You will be provided with dedicated access to a fully configured, GPU-accelerated workstation in the cloud.
Language: English
BINUS University
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DLI Ambassador, Bina Nusantara University
Wawan is an AI researcher whose focus is on the development of Deep Learning algorithms. He has led several research projects that utilize Deep Learning for Computer Vision, which is applied to various cases such as indoor video analytics and plant phenotyping. He has published over 50 peer-reviewed publications and reviewed for prestigious journals such as Scientific Reports and IEEE Access. He also holds 2 copyrights for AI-based video analytics software.
Bina Nusantara University
Bina Nusantara University, also known as BINUS University, is one of the best private higher education institutions in Indonesia. BINUS was awarded the title of “The Top 1 Private University in Indonesia” in the QS World University Rankings 2021 and included among “The Top 250 Universities” in the QS Asia University Rankings. With numerous awards under their belt, they have strived to develop the nation and build a global community through education and technology for many decades, mainly centralizing on the fields of Information Technology (IT). The journey of BINUS started back in 1974 as a computer course, until they are formally recognized as a private university in 1996. They then expanded and included other majors into their education programs. Throughout the years, more IT-related higher education majors were opened, including Cyber Security, Mobile Application & Technology, Game Application & Technology, and even Data Science. In 2017, BINUS finally established their own Artificial Intelligence Research & Development Center (AIRDC) in collaboration with NVIDIA and TechData. Through the AIRDC, many AI-related research have been conducted in various fields as an attempt to realize one of the many missions of BINUS, which is “Resolving the nation’s issues through high-impact research”. These fields of research include applications of AI and data science in agriculture, medical, genomics, proteomics, meteorology, video analytics, and many more.
Andry Chowanda is a Computer Science senior lecturer and Deputy Dean of the School of Computer Science at Bina Nusantara University, Indonesia. He received his Ph.D. degree in Computer Science from The University of Nottingham UK, a master’s degree in Business Management from BINUS Business School ID, and a bachelor’s degree in Computer Science from BINUS University ID. His research is in agent architecture and Machine (and Deep) Learning. His works mainly focus on how to model an agent that has the capability to sense and perceive the environment and react based on the perceived data in addition to the ability to build a social relationship with the user over time. In addition, Andry is also interested in serious games and gamification design.
As an AI researcher, Gregorius Natanael Elwirehardja have explored the usages of both conventional machine learning and deep learning in various fields. Currently, he is a researcher of NVIDIA-BINUS Artificial Intelligence Research & Development Center and a certified instructor of NVIDIA Deep Learning Institute. His research interests include applied machine learning in various fields including, but are not limited to, computer vision, mental health, and Natural Language Processing.