Introduction to NVIDIA NeMo - A Toolkit for Conversational AI | AISC

Introduction to NVIDIA NeMo - A Toolkit for Conversational AI | AISC

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
Subscribers:
22,300
Published on ● Video Link: https://www.youtube.com/watch?v=2kTZ0oST8wg


Duration: 1:01:43
5,518 views
82

For slides and more information on the paper, visit https://ai.science/e/ne-mo-introduction-to-nvidia-ne-mo--7qbdoCtyn7kZvtFkPE1K

Speaker: Christopher Parisien; Discussion Facilitator: Zach Nguyen, Gordon Gibson; Host: Alireza Darbehani

Motivation:
Building state-of-the-art conversational AI models requires researchers to quickly experiment with novel network architectures. This means going through the complex and time-consuming process of modifying multiple networks and verifying compatibility across inputs, outputs, and data pre-processing layers.

Learn more about NVIDIA NeMo: https://nvda.ws/3jKSU0T

NVIDIA NeMo is a Python toolkit for building, training, and fine-tuning GPU-accelerated conversational AI models using a simple interface. Using NeMo, researchers and developers can build state-of-the-art conversational AI models using easy-to-use application programming interfaces (APIs). NeMo runs mixed precision compute using Tensor Cores in NVIDIA GPUs and can scale up to multiple GPUs easily to deliver the highest training performance possible.



Other Videos By LLMs Explained - Aggregate Intellect - AI.SCIENCE


2021-02-17Author speaking: Proper Machine Learning Explanations through LIME using OptiLIME framework | AISC
2021-02-11Non-Euclidean Universal Approximation | AISC
2021-02-09Explainable AI with Layer-wise Relevance Propagation (LRP)
2021-02-04An Introduction to Quantum Computing
2021-02-04Predicting compound activity from phenotypic profiles
2021-02-03A Survey on the Explainability of Supervised Machine Learning
2021-01-29Reinforcement learning in sports analytics | AISC
2021-01-27We Can Measure XAI Explanations Better with Templates | AISC
2021-01-26Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods | AISC
2021-01-21Deep Learning in Healthcare and Its Practical Limitations
2021-01-14Introduction to NVIDIA NeMo - A Toolkit for Conversational AI | AISC
2021-01-14Explainable Classifiers Using Counterfactual Approach | AISC
2021-01-13Machine learning meets continuous flow chemistry: Automated process optimization | AISC
2021-01-12Screening and analysis of specific language impairment | AISC
2021-01-07High-frequency Component Helps Explain the Generalization of Convolutional Neural Networks | AISC
2021-01-06Locality Guided Neural Networks for Explainable AI | AISC
2021-01-05Explaining image classifiers by removing input features using generative models | AISC
2020-12-23An Introduction to the Quantum Tech Ecosystem | AISC
2020-12-22Explaining by Removing: A Unified Framework for Model Explanation | AISC
2020-12-17The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
2020-12-17Dirichlet Pruning for Neural Network Compression | AISC