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https://github.com/isymbo/awesome-fastai
A personal curated list for learning fast.ai and the related
https://github.com/isymbo/awesome-fastai
List: awesome-fastai
Last synced: about 1 month ago
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A personal curated list for learning fast.ai and the related
- Host: GitHub
- URL: https://github.com/isymbo/awesome-fastai
- Owner: isymbo
- Created: 2019-07-11T09:38:28.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2019-07-25T04:01:22.000Z (about 5 years ago)
- Last Synced: 2024-08-08T10:01:23.261Z (about 1 month ago)
- Size: 4.88 KB
- Stars: 4
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-fastai - A personal curated list for learning fast.ai and the related. (Other Lists / PowerShell Lists)
README
# Awesome fast.ai [](https://github.com/sindresorhus/awesome)
A personal curated list for learning fast.ai and the related
## Table of Contents
* **[Courses](#courses)**
* **[Videos and Lectures](#videos-and-lectures)**
* **[Papers](#papers)**
* **[Tutorials](#tutorials)**
* **[Websites](#websites)**
* **[Datasets](#datasets)**
* **[Frameworks](#frameworks)**
* **[Tools](#tools)**
* *[Research Papers](#research-papers)*
* *[Cheat Sheets](#cheat-sheets)*
* **[Miscellaneous](#miscellaneous)**
### Courses
### Videos and Lectures
### Papers
*You can also find the most cited deep learning papers from [here](https://github.com/terryum/awesome-deep-learning-papers)*
1. [BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding](https://arxiv.org/pdf/1810.04805.pdf)
* [BERT Explained: State of the art language model for NLP](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270)
* [Paper Dissected: “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” Explained](http://mlexplained.com/2019/01/07/paper-dissected-bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding-explained/)
* [BERT – State of the Art Language Model for NLP](https://www.lyrn.ai/2018/11/07/explained-bert-state-of-the-art-language-model-for-nlp/)
* [in Chinese: 从Word Embedding到Bert模型—自然语言处理中的预训练技术发展史](https://zhuanlan.zhihu.com/p/49271699)
2. [Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf)
* [Transformer: A Novel Neural Network Architecture for Language Understanding](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html)
* [Paper Dissected: “Attention is All You Need” Explained](http://mlexplained.com/2017/12/29/attention-is-all-you-need-explained/)
* [Attention Augmented Convolutional Networks](https://www.lyrn.ai/2019/05/03/attention-augmented-convolutional-networks/)
* [The Illustrated Transformer](http://jalammar.github.io/illustrated-transformer/)
* [Video: [Transformer] Attention Is All You Need | AISC Foundational](https://www.youtube.com/watch?v=S0KakHcj_rs)
* [Transformer Architecture: Attention Is All You Need](https://medium.com/@adityathiruvengadam/transformer-architecture-attention-is-all-you-need-aeccd9f50d09)
* [Paper in Two minutes: Attention Is All You Need](https://hub.packtpub.com/paper-in-two-minutes-attention-is-all-you-need/)
* [Let’s build ‘Attention is all you need’ — 1/2](https://medium.com/datadriveninvestor/lets-build-attention-is-all-you-need-1-2-de377cebe22)
* [Let’s build ‘Attention is all you need’ — 2/2](https://medium.com/datadriveninvestor/lets-build-attention-is-all-you-need-2-2-11d9a29219c4)
3. [Real-time Personalization using Embeddings for Search Ranking at Airbnb](https://www.kdd.org/kdd2018/accepted-papers/view/real-time-personalization-using-embeddings-for-search-ranking-at-airbnb)
* [Fantastic Embedding](https://medium.com/@fishlovebanana/fantastic-embedding-bbd37c32ca1f)
* [in Chinese: 不一样的论文解读2018 KDD best paper: Embeddings at Airbnb](https://zhuanlan.zhihu.com/p/49537461)
4. [BERT4Rec: Sequential Recommendation with Bidirectional
Encoder Representations from Transformer](https://arxiv.org/pdf/1904.06690.pdf)
### Tutorials
1. [Official Pytorch Tutorial](https://pytorch.org/tutorials/)
2. [Pytorch Tutorial by Yunjey Choi](https://github.com/yunjey/pytorch-tutorial)
### WebSites
1. [fast.ai](https://www.fast.ai/) The official fast.ai website
2. [Machine Learning Explained](https://mlexplained.com/) Deep learning, python, data wrangling and other machine learning related topics explained for practitioners
3. [lyrn.ai](https://www.lyrn.ai/) DEEP LEARNING EXPLAINED
4. [Papers with Code](https://paperswithcode.com/) a free and open resource with Machine Learning papers, code and evaluation tables
5. [Jay Alammar's Tech Blog](http://jalammar.github.io/) Visualizing machine learning one concept at a time
6. [mc.ai](https://mc.ai/) Aggregated news around AI and co
### Datasets
### Frameworks
1. [PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration](https://github.com/pytorch/pytorch)
### Tools
##### Research Papers
1. [Arxiv Sanity Preserver](http://www.arxiv-sanity.com/) Built in spare time by @karpathy to accelerate research
2. [Deep Learning Monitor](https://deeplearn.org/) help you to focus on your own research while monitoring the research field you care about
3. [SciHive](https://www.scihive.org) a free, open-source tool that is built by researchers for researchers
4. [IMPORT AI](https://jack-clark.net/) Import AI is a weekly newsletter about artificial intelligence
##### Cheat Sheets
1. [Python Cheatsheet](https://www.pythonsheets.com/)
2. [Python Crash Course - Cheat Sheets](https://ehmatthes.github.io/pcc/cheatsheets/README.html)
3. [Data Science Cheatsheets](https://www.datacamp.com/community/data-science-cheatsheets)
4. [7+ Python Cheat Sheets for Beginners and Experts](https://sinxloud.com/python-cheat-sheet-beginner-advanced/)
### Miscellaneous