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Learn how to design, develop, deploy and iterate on production-grade ML applications.
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
💿 Free software that works great, and also happens to be open-source Python.
A collection of various deep learning architectures, models, and tips
📡 Simple and ready-to-use tutorials for TensorFlow
Your new Mentor for Data Science E-Learning.
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
Code, Notebooks and Examples from Practical Business Python
Official content for Harvard CS109
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
An introduction to implementing a number of scikit-learn classifiers, along with some data exploration
This is a quick analysis on books suggestions from hacker news
Introducción a las nociones de Probabilidad Condicional y el Teorema de Bayes a través de dos aplicaciones: Localización y Naive Bayes