mlcourse_open - OpenDataScience Machine Learning course. Both in English and Russian

•    Python

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This is the list of published articles on medium.com 🇬🇧, habr.com 🇷🇺, and jqr.com 🇨🇳. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package. Assignments will be announced each week. Meanwhile, you can pratice with demo versions. Solutions will be discussed in the upcoming run of the course.

machine-learning data-analysis data-science pandas algorithms numpy scipy matplotlib seaborn plotly scikit-learn kaggle-inclass vowpal-wabbit ipynb docker math

machine_learning_basics - Plain python implementations of basic machine learning algorithms

•    Jupyter

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This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

machine-learning logistic-regression ipynb machine-learning-algorithms linear-regression perceptron python-implementations kmeans algorithm python3 neural-network k-nearest-neighbours k-nearest-neighbor k-nn neural-networks

computer-vision-in-action - 《计算机视觉实战演练：算法与应用》中文电子书、源码、读者交流社区（持续更新中

•    Jupyter

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《计算机视觉实战演练：算法与应用》中文电子书、源码、读者交流社区（持续更新中 ...） 📘 在线电子书 https://charmve.github.io/computer-vision-in-action/ 👇项目主页

machine-learning tutorial books computer-vision deep-learning neural-network notebook jupyter-notebook handbook pytorch transformer ipynb deep-learning-tutorial computer-vision-algorithms colab-notebook in-action charmve

python_intro - Jupyter notebooks in Russian

•    Jupyter

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В курсе рассматриваются основы програмирования на языке Python, а также есть материал про базовые алгоритмы и структуры данных. Более расширенная версия именно по основам Python – в этом репозитории курса ВШЭ "Интеллектуальный анализ данных". Курс разработан в виде тетрадок Jupyter - это удобное средство представления материала с интерактивным выполнением кода. Инструкции по локальному развертыванию сервера Jupyter для использования тетрадок представлены в тетрадке с обзором средств разработки.

python-lessons algorithms data-structures jupyter-notebook ipynb russian basics

pynamical - Pynamical is a Python package for modeling and visualizing discrete nonlinear dynamical systems, chaos, and fractals

•    Python

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pynamical uses pandas, numpy, and numba for fast simulation, and matplotlib for visualizations and animations to explore system behavior. Compatible with Python 2 and 3. Pynamical comes packaged with the logistic map, the Singer map, and the cubic map predefined. The models may be run with a range of parameter values over a set of time steps, and the resulting numerical output is returned as a pandas DataFrame. Pynamical can then visualize this output in various ways, including with bifurcation diagrams, two-dimensional phase diagrams, three-dimensional phase diagrams, and cobweb plots.

chaos nonlinear fractal logistic visualization modeling animation math physics pandas numba numpy matplotlib ipynb bifurcation-diagram fractals systems phase-diagram cobweb-plot

functional_intro_to_python - A functional, Data Science focused introduction to Python

•    HTML

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The first section is an intentionally brief, functional, data science centric introduction to Python. The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible. The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning and Linear Optimization to build systems and commandline tools.

python3 tutorial pandas jupyter-notebook functional-programming data-science learning-by-doing commandline spot-price machine-learning ipynb screencast optimization

react-ipython-notebook - React component for nbconvert.js

•    Javascript

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To see it in action, run $ npm start , go to http://localhost:8080, and drag an ipynb file onto the "Choose file" button.

ipython notebook ipynb react

FourierTalkOSCON - Presentation Materials for my "Sound Analysis with the Fourier Transform and Python" OSCON Talk

•    Jupyter

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Presentation Materials for my Sound Analysis with the Fourier Transform and Python OSCON 2013 Talk.

ipynb math fourier fourier-transform fourier-analysis

ipynb_notedown.vim - vim plugin for editing jupyter notebook (ipynb) files through notedown

•    Vim

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Vim/Neovim plugin for editing Jupyter notebook (ipynb) files through notedown. When you open a Jupyter Notebook (*.ipynb) file, it is automatically converted from json to markdown through the notedown utility. Upon saving the file, the content is converted back to the json notebook format.

vim-plugins jupyter-notebook ipynb

jupyter_demonstrations - Random jupyter notebooks on data analysis and machine learning

•    Jupyter

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Random jupyter notebooks to share. Mostly data analysis & machine learning. Those in Russian typically accompany the Habrahabr posts.

data-analysis visualization machine-learning jupyter-notebook pandas scikit-learn russian fun habrahabr-posts ipynb

tensorflow_deep_learning_models - TensorFlow implementations of several deep learning models (e

•    Jupyter

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This repository contains jupyter notebooks implementing several deep learning models using TensorFlow. Each notebook contains detailed explanations about each model, hopefully making it easy to understand all steps.

machine-learning deep-learning tensorflow rnn-tensorflow rnn cnn cnn-tensorflow vae variational-autoencoder recurrent-neural-networks recurrent-neural-network convolutional-neural-networks convolutional-neural-network notebook ipynb

find-path - Find path by using Semantic Segmentation.

•    Jupyter

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Find Path project finds humans paths and routes, such as sidewalks, park ways, forest paths. This project implements semantic segmentation approach. It uses VGG16 pretrained model. Go to calculations folder.

tensorflow dataset ipynb vgg16 semantic-segmentation fcn-16s dataset-maker