Lesson 3 - Multi-label, Segmentation, Image Regression, and More…
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Lesson resources
- Lesson notes from @PoonamV
- Detailed lesson notes by @hiromi
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The notebooks for this lesson require fastai 1.0.21 or later. Please
conda install -c fastai fastai(or the equivalent for your platform), and of course remember togit pullto get the latest notebooks - Notebooks:
- Lesson 3 in-class discussion
- Links to different parts in video by @melonkernel
Other resources
- Useful online courses for ML background:
– Introduction to Machine Learning for Coders taught by @jeremy
– Machine Learning taught by Andrew Ng (coursera) - Video Browser with Searchable Transcripts Password: deeplearningSF2018 (do not share outside the forum group) - PRs welcome.
- Quick and easy model deployment using Zeit Now
- Introduction to Kaggle API in Google Colab (Part-I) tutorial by @mmiakashs
- Data block API
- Python partials
- MoviePy Python module for video editing mentioned by @rachel
- WebRTC example for web video from @etown
- Nov 14 Meetup (wait list) Conversation between Jeremy Howard and Leslie Smith
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List of transforms in
vision.transformpackage - References in video
- Visual Explanation of The Universal Approximation Theorem for neural networks by Michael Nielsen
Further reading
- Cyclical Learning Rates for Training Neural Networks paper by Leslie Smith
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ULMFit fine-tuning for NLP Classification used in
language_model_learner() - Michael Nielsen’s book
