learning-machine
fastai
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| learning-machine | fastai | |
|---|---|---|
| 10 | 9 | |
| 486 | 25,577 | |
| - | 1.2% | |
| 0.0 | 8.0 | |
| 3 months ago | 9 days ago | |
| Python | 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.
learning-machine
- Show HN: ML Questions Answered
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Show HN: A Machine Learning Book: Learn ML by Reading Answers, Like So
Hi HN, original poster here!
We made a compilation (book) of questions that we got from 1300+ students from this course [1].
We believe that stackoverflow-like Q/A scheme is best for learning, so we made this.
Project Repo: https://github.com/rentruewang/learning-machine
Website: https://rentruewang.github.io/learning-machine
The website is hosted on GitHub, automatically built from the repo by github actions.
We are lucky to get some feedbacks on reddit here [2], here [3], and here [4], and have made changes accordingly. We really want to know what you guys on HN think. Any suggestions are welcome!
[1] https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.html
[2] https://www.reddit.com/r/datascience/comments/oz7xab/open_so...
[3] https://www.reddit.com/r/learnmachinelearning/comments/oz78n...
[4] https://www.reddit.com/r/MachineLearning/comments/oz7p26/p_o...
- Show HN: A Machine Learning Book: Learn ML by Reading Answers, Like SO
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Open Sourced a Machine Learning Book: Learn Machine Learning By Reading Answers, Just Like StackOverflow
[Project Repo](https://github.com/rentruewang/learning-machine)
- Learn machine learning by reading answers to questions, like stack overflow.
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Learn ML by answers to other people's questions!
Website Project Repo.
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Learn machine learning by reading someone else's questions answered.
We are working on a compilation of frequently asked questions! We aim to answer beginner-unfriendly questions in a simple, and strait-forward way so that it will never be asked again. Hope you find this helpful! Website, Project Repo.
- Show HN: A handbook to help students learn machine learning
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
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.