fastai
handson-ml3
fastai | handson-ml3 | |
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9 | 6 | |
25,638 | 6,151 | |
0.6% | - | |
8.0 | 6.8 | |
11 days ago | 15 days ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fastai
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Cleared AWS Machine Learning - Specialty exam.. Happy to help!!!
Jeremy Howard's YouTube Channel - Jeremy maintains the fastai library, which is an excellent package that will help anyone build complicated ML architectures in minimum time. His YouTube Channel has a number of free courses which do an amazing job of covering a variety of ML topics, and he also maintains a very active forum for people studying ML.
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Coding your own AI in 2023 with fastai
To create the AI we will use fastai. This is a python library, which is build on top of pytorch. No worries, you don't need to know how to code python. We will learn how this stuff works along the way :)
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Fast.ai starts a corporate partnership program
You may know fast.ai as a popular deep learning course. There is also a deep learning library with the same name (https://github.com/fastai/fastai) as well as software development tools like nbdev (https://nbdev.fast.ai/).
fast.ai has been offering education and tools for free for over 7 years, and has been approached by many companies asking for help. This program offers an avenue for business to get relevant professional services and support.
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People tricking ChatGPT “like watching an Asimov novel come to life”
The "fastai" course is free, and does a really nice job walking you through building simple neural nets from the ground up:
https://github.com/fastai/fastai
What's going on here is the exact same thing, just much, much larger.
- Programação letrada com Jupyter Notebook e Nbdev
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Why noone uses nbdev for library development?
Development NB: https://github.com/fastai/fastai/blob/master/nbs/09_vision.augment.ipynb
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[D] What Repetitive Tasks Related to Machine Learning do You Hate Doing?
There is already a ton of momentum around automating ML workflows. I would suggest you contribute to a preexisting project like, for instance, PyTorch Lightning or fast.ai.
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Good practices for neural network training: identify, save, and document best models
If you are unaware of what fastai is, its official description is:
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D I Refuse To Use Pytorch Because Its A Facebook
Also, not a single docstring to document any code in the library - https://github.com/fastai/fastai/blob/master/fastai/vision/learner.py
handson-ml3
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Machine Learning Models: Linear Regression
Hands On Machine Learning 3rd Edition Github — https://github.com/ageron/handson-ml3/tree/main
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What is the difference between the 2nd and 3rd eds of Hands-On Machine Learning??
Changes between 2nd and 3rd edition
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A useful ML-geared linear algebra tutorial in Python
I’m reading Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron, and I noticed that he included a whole Jupyter Notebook on linear algebra in the book’s accompanying GitHub page. Given how frequently there seem to be people asking about where to learn linear algebra on here, I thought I’d share in case anyone finds it useful (there’s also a differential calculus notebook in there too, which is great).
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Mobile version of hands on ml
I think all of you know about hands-on machine learning. If not, this is what I am talking about: https://github.com/ageron/handson-ml3
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Cleared AWS Machine Learning - Specialty exam.. Happy to help!!!
Hands on ML by Aurélien Géron - A lot of people regard this as one of the best introductions to ML, and I won't disagree. There are very good Jupyter notebooks for all of the topics in the book. The only drawback to this book is that there's virtually no mention of AWS services - the author used to work for YouTube, so whenever he talks about cloud computing he references Google Cloud Platform services.
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Is it a good start for beginners? if not what would you recommend?
Not so much. The changes are documented here: https://github.com/ageron/handson-ml3/blob/main/CHANGES.md The repo also contains the code samples of each chapter for a more detailed comparison.
What are some alternatives?
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
fastbook - The fastai book, published as Jupyter Notebooks
Watermark-Removal-Pytorch - 🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.
PySyft - Perform data science on data that remains in someone else's server
lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)
ru-dalle - Generate images from texts. In Russian
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
iterative-grabcut - This algorithm uses a rectangle made by the user to identify the foreground item. Then, the user can edit to add or remove objects to the foreground. Then, it removes the background and makes it transparent.
catam-julia - CATAM material in Julia
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
pytorch-deepdream - PyTorch implementation of DeepDream algorithm (Mordvintsev et al.). Additionally I've included playground.py to help you better understand basic concepts behind the algo.
entity-embed - PyTorch library for transforming entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors.