Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
- machine-learning
- models
- ui
- ui-components
Repository Index
47,133 repositories indexed
1–24 of 364 · Most stars
Google Research
A game theoretic approach to explain the output of any machine learning model.
- interpretability
- machine-learning
- deep-learning
- gradient-boosting
A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
- notebooks
- python
- data-science
- machine-learning
Interactive Data Visualization in the browser, from Python
- bokeh
- python
- interactive-plots
- javascript
The interactive graphing library for Python :sparkles:
Data and code behind the articles and graphics at FiveThirtyEight
- data
The Cloud Native AI Platform
- ml
- kubernetes
- minikube
- tensorflow
stable diffusion webui colab
- stable-diffusion
- stable-diffusion-web-ui
- stable-diffusion-webui
- colab
StableLM: Stability AI Language Models
This repository contains implementations and illustrative code to accompany DeepMind publications
Jupyter Interactive Notebook
- closember
- jupyter
- jupyter-notebook
- notebook
Kubernetes Community Documentation
The repository host gives no description.
Automatic extraction of relevant features from time series:
- data-science
- feature-extraction
- time-series
Ready-to-run Docker images containing Jupyter applications
Multi-user server for Jupyter notebooks
- jupyter-notebook
- jupyterhub
- python
- multi-user
Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
- jupyter-notebook
- jupyterlab-extension
- version-control
- knitr
📚 Parameterize, execute, and analyze notebooks
- jupyter
- notebooks
- notebook-generator
- nteract
A scikit-learn compatible neural network library that wraps PyTorch
Voilà turns Jupyter notebooks into standalone web applications
- jupyter
- jupyter-notebook
- jupyterlab-extension
- dashboarding
Automate code & data workflows with interactive Elixir notebooks
- phoenix
- elixir
- notebooks
- collaborative
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
Create delightful software with Jupyter Notebooks
- jupyter-notebooks
- python
- literate-programming
- fastai