Neural Network Pruning and Training with Jonathan Frankle at MosaicML
Show notes
Jonathan Frankle, Chief Scientist at MosaicML and Assistant Professor of Computer Science at Harvard University, joins us on this episode. With comprehensive infrastructure and software tools, MosaicML aims to help businesses train complex machine-learning models using their own proprietary data. We discuss: - Details of Jonathan’s Ph.D. dissertation which explores his “Lottery Ticket Hypothesis.” - The role of neural network pruning and how it impacts the performance of ML models. - Why transformers will be the go-to way to train NLP models for the foreseeable future. - Why the process of speeding up neural net learning is both scientific and artisanal. - What MosaicML does, and how it approaches working with clients. - The challenges for developing AGI. - Details around ML training policy and ethics. - Why data brings the magic to customized ML models. - The many use cases for companies looking to build customized AI models. Jonathan Frankle - https://www.linkedin.com/in/jfrankle/ Resources: - https://mosaicml.com/ - The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation. #OCR #DeepLearning #AI #Modeling #ML
Guests
Jonathan Frankle
Chief AI Scientist, Databricks
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