Local AI app and developer runtime for running, chatting with, and serving open models privately.
Fast.ai
This is a clip from a conversation with Jeremy Howard on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2NG4qwr If you enjoy these, consider subscribing, sharing, and commenting below.
Full episode: http://bit.ly/2NG4qwr
Full episodes playlist: http://bit.ly/2EcbaKf
Clips playlist: http://bit.ly/2JYkbfZ
Podcast website: https://lexfridman.com/ai
Jeremy Howard is the founder of fast.ai, a research institute dedicated to make deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, a former president of Kaggle as well a top-ranking competitor there, and in general, he's a successful entrepreneur, educator, research, and an inspiring personality in the AI community.
Subscribe to this YouTube channel or connect on:
- Twitter: https://twitter.com/lexfridman
- LinkedIn: https://www.linkedin.com/in/lexfridman
- Facebook: https://www.facebook.com/lexfridman
- Instagram: https://www.instagram.com/lexfridman
- Medium: https://medium.com/@lexfridman
- Support on Patreon: https://www.patreon.com/lexfridman

What to know
Key features
- High-level PyTorch wrapper for rapid prototyping
- Integrated deep learning courses for all skill levels
- Layered API allowing seamless transition from high-level to low-level code
- Optimized training loops with built-in learning rate finders
- Support for computer vision, NLP, and tabular data
- Comprehensive documentation and community-driven forums
Best for
- Learning deep learning fundamentals through a top-down approach
- Rapidly building and testing computer vision models
- Training sentiment analysis or text classification models
- Analyzing and predicting outcomes from tabular datasets
- Academic research into deep learning optimization
Pros
- Extremely accessible for beginners with a 'top-down' learning style
- Powerful high-level abstractions that speed up development
- Completely free and open-source with an active community
Cons
- High-level abstractions can hide underlying complexity, making debugging harder for novices
- Tight coupling with PyTorch may be a limitation for users of other frameworks
- Documentation can occasionally be fragmented between courses and API docs
Fast.ai FAQ
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