Hands-on Medium Deep learning Applications in real-case, ex: Kaggle kernels Includes more advanced technics used in Machine Learning and the specific tools for large data wrangling and also faster analysis with TPU processing.
Opensourced: Added Advanced visualization such as maps, and interactive dashboards, developed models on TPU, GPU, with 95+ accuracy
Include: Folium JSON Tensorflow Pytorch
- Geospatial Analysis
- Tensor Processing Unit Application
- Automated Machine Learning Applications
- Advanced Feature Engineering
- Explatory Data Analysis
- Implementation of Famous Research papers
- Top Ranked EDA, Modelling and Inference Notebooks from Kaggle competitions
To use the template copy the contents of README-template.md, save it as README.md in the root of your project, and use your text editor to edit the document as necessary.
Further documentation, comment, in the file itself (ipython notebook, ipynb)
curl https://raw.githubusercontent.com/ascott1/readme-template/master/README-template.md > README.md
If you have questions or need further guidance on using this template, please file an issue. I will do my best to respond to all issues in a timely manner.
All contributions and suggestions are welcome!
For suggested improvements, please file an issue.
For direct contributions, please fork the repository and file a pull request. If you never created a pull request before, welcome 🎉 😄 Here is a great tutorial on how to send one.
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This project pledges to follow the Contributor's Covenant.
This project is licensed under The Unlicense and released to the Public Domain. For more information see our LICENSE file.