Data science and machine learning research and tools move quickly. This is a place to attempt to keep a somewhat organized list of resources that I have found useful or look interesting. Essentially a glorified bookmarks page that is hopefully better organized, and has better descriptions.
- arxiv, but better: arxiv-sanity-preserver
- Google research: link
- FaceBook AI research link
- Chollet GitHub
- Kaggle Competition
- Explainer Video - Yannic Kilcher
- Neural Networks to solve IQ tests using Scattering Compositional Learner (SCL): paper
- Creating graphical representations of cloud architecture with a Python package: diagrams.mingrammer
- Cookie cutter to have consistent and reasonable project structures for DS/ML projects: data-science-cookie-cutter
- Developing a reusable networking class and why writing reusable code is important: blog
- Deep Learning Specialization, Andrew Ng: link
- Andrew Ng's Machine Learning: link
- AWS Machine Learning: link
- A Cloud Guru
- Hands On Machine Learning
- Deep Learning with Python
- Data Science From Scratch
- Introduction to Machine Learning with Python
- Python for Data Analysis
- Deep Learning
- The Elements of Statistical Learning
- Python Data Science Handbook
While these libraries are useful tools by themselves, they also provide great templates for structuring code and projects
- Lime
- Shap
- shapash
- TF Explain
- CNN Localization
- Turn any CNN image classifier into an object detector with Keras, TF, and OpenCV: blog
- Cool website that allows you to view what is happening inside an MLP: link
- matplotlib
- Altair
- YellowBrick
- Create gifs from Altair, matplotlib, and plotly animations: repo
- auto ml overview: link
These would be interesting to attend, but also useful when looking for interesting youtube videos.
- International Conference on Machine Learning ICML
- Neural Information Processing Systems NeurIPS
- International Conference on Learning Representations ICLR
- Conference in Computer Vision and Pattern Recognition CVPR
- Association for Computational Linguistics ACL
- AWS
- GCP
- Azure
- Kubernetes While this is a cloud agnostic tool, it is very cloud oriented.
- Introduction to AWS Lambda, layers, and boto3: link
- Docker for Machine Learning: blog
- Article comparing GPU performance: TimDettmers
- Get compatibility of hardware components: link
- Terry Tao's blog
- Geometry and the imagination: blog
- Sketches of topology: blog
- SuperMathematics Space Oddity
- Simons Foundation
- Math Stack Exchange
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent