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👉A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
😎TOPICS: ``
⭐️STARS：10427, 今日上升数↑:22
Note: If you are looking for the first edition notebooks, check out ageron/handson-ml.
Quick Start
Want to play with these notebooks online without having to install anything?
Use any of the following services.
WARNING: Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about.
Recommended: open this repository in [Colaboratory](ht...
👉[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS). It is also a PyTorch implementation of the NeurIPS 2020 paper 'Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect'.
😎TOPICS: ``
⭐️STARS：127, 今日上升数↑:15
👉README：
A Strong Single-Stage Baseline for Long-Tailed Problems
This project provides a strong single-stage baseline for Long-Tailed Classification (under ImageNet-LT, Long-Tailed CIFAR-10/-100 datasets), Detection, and Instance Segmentation (under LVIS dataset). It is also a PyTorch implementation of the NeurIPS 2020 paperLong-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect, which proposes a general solution to remove the bad momentum causal effect for a variety of Long-Tailed Recognition tasks. The codes are organized into three folders:
The classification folder supports long-tailed classification on ImageNet-LT, Long-Tailed CIFAR-10/CIFAR-100 datasets.
The lvis_old folder (deprecated) supports long-tailed object detection and instance segmentation on LVIS V0.5 dataset, which is built on top of mmdet V1.1.
The latest version of long-tailed detection and instance segmentation is under [lvis1.0...
👉Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).
😎TOPICS: umap,dimensionality-reduction,semisupervised-learning,representation-learning,machine-learning
⭐️STARS：67, 今日上升数↑:11
👉README：
Parametric UMAP (2020; Code for paper)
This repository contains the code needed to reproduce the results in the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" by Sainburg, McInnes, and Gentner (2020).
Citation:
@Article{parametricumap,
title={Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning},
author={Sainburg, Tim and McInnes, Leland and Gentner, Timothy Q},
}
How to use
The main implementation of this code is available in umap.parametric_umap in the UMAP repository (v0.5+). Most people reading this will want to use that code, and can ignore this repository.
The code in this repository is the 'messy' version. It has custom training loops which are a bit more verbose and customizable. It might be more useful for integrating UMAP into your custom models.
👉A beginner-friendly project to help you in open-source contributions. Made specifically for contributions in HACKTOBERFEST 2020! Algorithms in Python and Machine Learning. Please leave a star ⭐ to support this project! ✨
😎TOPICS: hactoberfest,hactoberfest2020,first-timers,first-pull-request,first-pull-request-and-commit,first-contributions,good-first-issue,open-source,beginner,beginner-friendly,digitalocean,easy-to-use,github,up-for-grabs,machine-learning,python,python3,machinelearning,pr-welcome
⭐️STARS：38, 今日上升数↑:13
👉README：
A beginner friendly project to help you in open source contributions. An attempt to bring all the algorithms together.
The goal of this project is to help the beginners with their contributions in Open Source and bring all the possible algorithms of Machine Learning and Python together. We aim to achieve this collaboratively, so feel free to contribute in any way you want, just make sure to follow the contribution guidelines.
For now, this repo is focused on the beginner friendly contributions in Hacktoberfest 2020.
The open source community provides a great opportunity for aspiring...
👉Kubernetes community content
😎TOPICS: kubernetes
⭐️STARS：7036, 今日上升数↑:6
👉README：
Kubernetes Community
Welcome to the Kubernetes community!
This is the starting point for joining and contributing to the Kubernetes community - improving docs, improving code, giving talks etc.
To learn more about the project structure and organization, please refer to [Project Governance] information.
Communicating
The communication page lists communication channels like chat,
issues, mailing lists, conferences, etc.
For more specific topics, try a SIG.
Governance
Kubernetes has the following types of groups that are officially supported:
Committees are named sets of people that are chartered to take on sensitive topics.
This group is encouraged to be as open as possible while achieving its mission but, because of the nature of the topics discussed, private communications are allowed.
Examples of committees include the steering committee and things like security or code of conduct.
Special Interest Groups (SIGs) are persistent open groups that focus on a ...
If you are looking to learn TensorFlow, don't miss the core TensorFlow documentation
which is largely runnable code.
Those notebooks can be opened in Colab from tensorflow.org.
What is this repo?
This is the TensorFlow example repo. It has several classes of material:
👉Repository for the free online book Machine Learning from Scratch (link below!)
😎TOPICS: ``
⭐️STARS：317, 今日上升数↑:38
👉README：
Machine Learning from Scratch
Welcome to the repo for my free online book, "Machine Learning from Scratch".
The book itself can be found here.
(A somewhat ugly version of) the PDF can be found in the book.pdf file above in the master branch. N...
🤩Python随身听-技术精选： /ageron/handson-ml2
👉README：
Machine Learning Notebooks
This project aims at teaching you the fundamentals of Machine Learning in
python. It contains the example code and solutions to the exercises in the second edition of my O'Reilly book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow:
Note: If you are looking for the first edition notebooks, check out ageron/handson-ml.
Quick Start
Want to play with these notebooks online without having to install anything?
Use any of the following services.
WARNING: Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about.
地址：https://github.com/ageron/handson-ml2
🤩Python随身听-技术精选： /Pierian-Data/Complete-Python-3-Bootcamp
👉README：
Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
Get it now for ...
地址：https://github.com/Pierian-Data/Complete-Python-3-Bootcamp
🤩Python随身听-技术精选： /Atcold/pytorch-Deep-Learning
👉README：
This notebook repository now has a companion website, where all the course material can be found in video and textual format.
🇬🇧 🇨🇳 🇰🇷 🇪🇸 🇮🇹 🇹🇷 🇯🇵 [🇸🇦](https://github.com/Atcold/pytorch-Deep-Learning/blob/master/docs/ar/README-AR.m...
地址：https://github.com/Atcold/pytorch-Deep-Learning
🤩Python随身听-技术精选： /KaihuaTang/Long-Tailed-Recognition.pytorch
👉README：
A Strong Single-Stage Baseline for Long-Tailed Problems
This project provides a strong single-stage baseline for Long-Tailed Classification (under ImageNet-LT, Long-Tailed CIFAR-10/-100 datasets), Detection, and Instance Segmentation (under LVIS dataset). It is also a PyTorch implementation of the NeurIPS 2020 paper Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect, which proposes a general solution to remove the bad momentum causal effect for a variety of Long-Tailed Recognition tasks. The codes are organized into three folders:
地址：https://github.com/KaihuaTang/Long-Tailed-Recognition.pytorch
🤩Python随身听-技术精选： /timsainb/ParametricUMAP_paper
👉README：
Parametric UMAP (2020; Code for paper)
This repository contains the code needed to reproduce the results in the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" by Sainburg, McInnes, and Gentner (2020).
Citation:
@Article{parametricumap,
title={Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning},
author={Sainburg, Tim and McInnes, Leland and Gentner, Timothy Q},
}
How to use
The main implementation of this code is available in
umap.parametric_umapin the UMAP repository (v0.5+). Most people reading this will want to use that code, and can ignore this repository.The code in this repository is the 'messy' version. It has custom training loops which are a bit more verbose and customizable. It might be more useful for integrating UMAP into your custom models.
The code can be installed with `py...
地址：https://github.com/timsainb/ParametricUMAP_paper
🤩Python随身听-技术精选： /geekquad/AlgoBook
👉README：
A beginner friendly project to help you in open source contributions. An attempt to bring all the algorithms together.Please see the Contributing Guidelines .
Join the community on Slack.
Overview
The goal of this project is to help the beginners with their contributions in Open Source and bring all the possible algorithms of Machine Learning and Python together. We aim to achieve this collaboratively, so feel free to contribute in any way you want, just make sure to follow the contribution guidelines.
For now, this repo is focused on the beginner friendly contributions in Hacktoberfest 2020.
The open source community provides a great opportunity for aspiring...
地址：https://github.com/geekquad/AlgoBook
🤩Python随身听-技术精选： /kubernetes/community
👉README：
Kubernetes Community
Welcome to the Kubernetes community!
This is the starting point for joining and contributing to the Kubernetes community - improving docs, improving code, giving talks etc.
To learn more about the project structure and organization, please refer to [Project Governance] information.
Communicating
The communication page lists communication channels like chat,
issues, mailing lists, conferences, etc.
For more specific topics, try a SIG.
Governance
Kubernetes has the following types of groups that are officially supported:
This group is encouraged to be as open as possible while achieving its mission but, because of the nature of the topics discussed, private communications are allowed.
Examples of committees include the steering committee and things like security or code of conduct.
地址：https://github.com/kubernetes/community
🤩Python随身听-技术精选： /tensorflow/docs
👉README：
TensorFlow Documentation
These are the source files for the guide and tutorials on
tensorflow.org.
To contribute to the TensorFlow documentation, please read
CONTRIBUTING.md, the
TensorFlow docs contributor guide,
and the style guide.
To file a docs issue, use the issue tracker in the
[tensorflow/tensorflow](https://github.com/tensorflow/tensorflow/issues/new?template=20-docume...
地址：https://github.com/tensorflow/docs
🤩Python随身听-技术精选： /tensorflow/examples
👉README：
TensorFlow Examples
Most important links!
If you are looking to learn TensorFlow, don't miss the
core TensorFlow documentation
which is largely runnable code.
Those notebooks can be opened in Colab from
tensorflow.org.
What is this repo?
This is the TensorFlow example repo. It has several classes of material:
地址：https://github.com/tensorflow/examples
🤩Python随身听-技术精选： /Mikoto10032/DeepLearning
👉README：
DeepLearning Tutorial
一. 入门资料
完备的 AI 学习路线，最详细的中英文资源整理 ⭐
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NL
Machine-Learning
数学基础
机器学习基础
快速入门
地址：https://github.com/Mikoto10032/DeepLearning
🤩Python随身听-技术精选： /dafriedman97/mlbook
👉README：
Machine Learning from Scratch
Welcome to the repo for my free online book, "Machine Learning from Scratch".
The book itself can be found here.
(A somewhat ugly version of) the PDF can be found in the book.pdf file above in the
masterbranch. N...地址：https://github.com/dafriedman97/mlbook
🤩Python随身听-技术精选： /rasbt/deeplearning-models
👉README：
Deep Learning Models
A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
Traditional Machine Learning
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | [Nbviewer](https://nbviewer.jupyter.org/github/rasbt/deeplearnin...
地址：https://github.com/rasbt/deeplearning-models
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