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# New Resourcecs for Review
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https://github.com/vinta/awesome-python
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Machine Learning Algorithms from scratch
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https://github.com/Gautam-J/Machine-Learning
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[Curated NLP Database List of 1000+ Natural Language Processing Datasets](https://metatext.io/datasets)
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https://github.com/jwasham/coding-interview-university
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https://github.com/aj-ames/Centerface-Deepstream
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https://github.com/kyclark/tiny_python_projects
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https://github.com/ageron/handson-ml2
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https://github.com/visenger/awesome-mlops
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https://github.com/garrettj403/SciencePlots
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https://github.com/whythawk/data-as-a-science
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https://github.com/TheAlgorithms/Python
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https://github.com/eugeneyan/applied-ml
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https://github.com/Rishit-dagli/Perceiver
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https://github.com/aj-ames/Activity-Recognition-TensorRT
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### Over 200 figures and diagrams of the most popular deep learning architectures and layers FREE TO USE in your blog posts, slides, presentations, or papers.
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https://github.com/dvgodoy/dl-visuals
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https://jakevdp.github.io/PythonDataScienceHandbook/
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https://github.com/huggingface/autonlp
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https://github.com/sayakpaul/Denoised-Smoothing-TF
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Maths https://github.com/Developer-Y/cs-video-courses#math-for-computer-scientist
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The algorithms https://github.com/TheAlgorithms/Python
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https://github.com/open-mmlab/mmdetection/tree/master/configs/fast_rcnn
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