- General
- Artificial Intelligence
- Automation
- Ethics / altruistic motives
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- Mathematics, Statistics, Probability & Probabilistic programming
- Data
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- Examples
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- Articles, papers, code, data, courses
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- Contributing
- Demystification of the key concepts of Artificial Intelligence and Machine Learning
- 12 thought leaders on LinkedIn who are creating original content to learn Artificial Intelligence and Machine Learning
- AI Repository by Goku Mohandas
- See Artificial Intelligence
- Automated Machine Learning — An Overview
- Automated pipelines
- Automated machine learning tools (or partial AutoML tools)
- Automated Machine Learning - Google search results
- Recipes for Driverless AI
- [PyCaret Tutorial Using Titanic Dataset](https://www.kaggle.com/ravileo/pycaret-tutorial-using-titanic-dataset](https://towardsdatascience.com/announcing-pycaret-an-open-source-low-code-machine-learning-library-in-python-4a1f1aad8d46)
- [PyCaret Demo](https://pycaret.org/demo/](https://github.com/pycaret/pycaret-demo-dataraction)
- Running Low on Time? Use PyCaret to Build your Machine Learning Model in Seconds
- Libra • Automates the end-to-end machine learning process in just one line of code: GitHub | Notebooks with tutorials | Docs | NLP Queries
- GitHub is the best AutoML you will ever need 👇 👇 👇
- AutoGOAL: an autoML framework (high & low level) by Alejandro Piad et al.
- OttoML - Otto makes machine learning an intuitive, natural language experience.
- TPOT for Automated Machine Learning in Python
- Data science competitions to build a better world
- An ethics checklist for data scientists
- 👉A Practical guide to Responsible Artificial Intelligence (AI) by PwC 👈
- Data ethics literacy cards by Anisha Fernando | Join the Slack community | The Private Lives of Data: YouTube video
- [Ethics in Artificial Intelligence](https://www.linkedin.com/posts/vincentg_ethics-in-artificial-intelligence-activity-6690365775091445761-U5qh
- robotethics | aiethics.ai | AIethics.AI – Artificial Intelligence and Robot Ethics
- UK gov’s guidance
- Google principles
See Java
See JavaScript
See Visualisation
See Mathematica & Wolfram Language
See Mathematics, Statistics, Probability & Probabilistic programming
- Do we know our data...
- Data Science at the Command Line | References | on GitHub | Docker image with 80 CLI tools | Appendix: List of Command-Line Tools | Linux Command-Line resource by Chris Albon
- Awesome Datascience
- Awesome Learn Datascience
- Data Science for Dummies
- Data Science resources (scattered across the page)
- Learn Data Science by bitgrit
- and other related topics: Stats, Visualisations, Cheatsheets, etc...
- How can I become a data scientist?
- Being a Data Science Contractor - UK: How to find work?
- How to switch career from Automation Testing to Data Science? Here is a simple guide.
- 9 Mistakes to avoid when starting your career in Data Science
- How can I become a data scientist?
- 8 essential tools for data scientists
- Data Scientist is not One-Man-Army, but should know some tech concept, not mandatory to master (depend on the company), this is what I choose
- The Ultimate Learning Path to Become a Data Scientist and Master Machine Learning
♦️ MUST READ ARTICLES FOR DATA SCIENCE ENTHUSIAST♦️
- PyTorch Geometric Temporal - temporal extensions PyTorch Geometric Benedek Rozemberczki
- A number of interesting links on Graph Networks by Yaz
- Graph Representation Learning Book • The field of graph representation learning has grown at an incredible (and sometimes unwieldy)
- Daniele Grattarola gave a great talk on his graph machine learning library Spektral. Learn how to create graph neural networks (GNNs) with ease
- Towards Deeper Graph Neural Networks • Deep Adaptive Graph Neural Network (DAGNN) can be used to learn graph node representations from larger receptive fields.
- Graph-Powered Machine Learning • Free eBook Excerpt (Chapter: 3, 4, 7)
- Cytoscape interactive network visualization in Python and Dash. A graph visualization component for creating easily customizable, high-performance, interactive, and web-based networks.
- Notes on graph theory — Centrality measures by Anas AIT AOMAR
- COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce - A new dataset for conversational recommendation over knowledge graphs in e-commerce platforms.
- @plotlygraphs We’ve explored @OpenAI’s new #GPT3 API, and we are super impressed with its capabilities!
- Natural Graph Networks • Conventional neural message passing algorithms are invariant under permutation of the messages and hence forget how the information flows through the network.
- Extracting knowledge from knowledge graphs using Facebook Pytorch BigGraph
- Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
- Graph Programming by Uri Valevski https://bit.ly/3nhZr4w
- Open Graph Benchmark: Datasets for Machine Learning on Graphs -
- BCS APSG - 2019 02 14 How Graph Technology is Changing AI and ML at BCS London
- Graph databases
- See the Grakn example in the
examples/data/databases/graph/graknfolder
- See the Grakn example in the
See Notebooks
- Model Zoo - Discover open source deep learning code and pretrained models
- Model Zoo: Caffe docs | Caffe | MXNet | DL4J | CoreNLP
See Articles, papers, code, data, courses
See Other Tools
- "nn" things every Java Developer should know about AI/ML/DL
- From backend development to machine learning
- NLP presentations
- Data presentations
- Best Practices for ML Engineering by Martin Zinkevich
- See also Best practices / rules / an unordered list of high level or low level guidelines
See Cheatsheets
See Misc
Contributions are very welcome, please share back with the wider community (and get credited for it)!
Please have a look at the CONTRIBUTING guidelines, also have a read about our licensing policy.
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