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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 ;)
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Python programs, usually short, of considerable difficulty, to perfect particular skills.
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
A High-Quality Real Time Upscaler for Anime Video
📡 Simple and ready-to-use tutorials for TensorFlow
A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding). Translations: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷 🇪🇸
One has no future if one couldn't teach themself.
wtfpython的中文翻译/施工结束/ 能力有限，欢迎帮我改进翻译
The "Python Machine Learning (1st edition)" book code repository and info resource
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
Automatic extraction of relevant features from time series:
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K…
Python code for "Probabilistic Machine learning" book by Kevin Murphy
A course in reinforcement learning in the wild
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
A better notebook for Scala (and more)
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Think DSP: Digital Signal Processing in Python, by Allen B. Downey.
The Jupyter Notebooks behind my OReilly report, "A Whirlwind Tour of Python"
Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.
Lectures on scientific computing with python, as IPython notebooks.
