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👉Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.
The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible
but rather to present the inner workings of them in a transparent and accessible way.
➜ apt-get install vim
E: Could not open lock file /var/lib/dpkg/lock - open (13: Permission denied)
E: Unable to lock the administration directory (/var/lib/dpkg/), are you root?
➜ fuck
sudo apt-get install vim [enter/↑/↓/ctrl+c]
[sudo] password for nvbn:
Reading package lists... Done
...
➜ git push
fatal: The current branch master has no upstream branch.
To push the current branch and set the remote as upstream, use
BERTopic is a topic modeling technique that leverages BERT embeddings and c-TF-IDF to create dense clusters
allowing for easily interpretable topics whilst keeping important words in the topic descriptions.
The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users
can take full advantage of TensorFlow for their research and product development.
• A collection of example implementations for SOTA models using the latest TensorFlow 2's high-level APIs • Officially maintained, supported, and kept up to date with the latest TensorFlow 2 APIs by TensorFlow • Reasonably optimized for fast performance while still being easy to read
Disclaimer: This project should be used for authorized testing or educational purposes only.
BYOB is an open-source post-exploitation framework for students, researchers and developers. It includes features such as:
Pre-built C2 server
Custom payload generator
12 post-exploitation modules
It is designed to allow students and developers to easily implement their own code and add cool new
features without having to write a C2 server or Remote Administration Tool from scratch.
This project has 2 main parts: the original console-based application (/byob) and the web GUI (/web-gui).
Web GUI
Dashboard
A control panel for your C2 server with a point-and-click interface for executing post-exploitation modules. The control panel includes an interactive map of client machines and a dashboard...
For information on contributing to this project, please see the contributing guide.
Please note a passing build status indicates all listed APIs are available since the last update. A failing build status indicates that 1 or more services may be unavailable at the moment.
This project builds a self-driving RC car using Raspberry Pi, Arduino and open source software. Raspberry Pi collects inputs from a camera module and an ultrasonic sensor, and sends data to a computer wirelessly. The computer processes input images and sensor data for object detection (stop sign and traffic light) and collision avoidance respectively. A neural network model runs on computer and makes predictions for steering based on input images. Predictions are then sent to the Arduino for RC car control.
👉A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
😎TOPICS: ``
⭐️STARS：519, 今日上升数↑:17
👉README：
FrankMocap: A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
FrankMocap pursues an easy-to-use single view 3D motion capture system developed by Facebook AI Research (FAIR). FrankMocap provides state-of-the-art 3D pose estimation outputs for body, hand, and body+hands in a single system. The core objective of FrankMocap is to democratize the 3D human pose estimation technology, enabling anyone (researchers, engineers, developers, artists, and others) can easily obtain 3D motion capture outputs from videos and images.
Recognize and manipulate faces from Python or from the command line with
the world's simplest face recognition library.
Built using dlib's state-of-the-art face recognition
built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark.
This also provides a simple face_recognition command line tool that lets
you do face recognition on a folder of images from the command line!
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
We present a generic image-to-image translation framework, Pixel2Style2Pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the extended W+ latent space. We first show that our encoder can directly embed real images into W+, with no additional optimization. We further introduce a dedicated identity loss which is shown to achieve improved performance in the reconstruction of an input image. We demonstrate pSp to be a simple architecture that, by leveraging a well-trained, fixed generator network, can be easily applied on a wide-range of image-to-image translation tasks. Solving these t...
The most advanced version of DeOldify image colorization is available here, exclusively. Try a few images for free! MyHeritiage In Color
Image (artistic) |
Video
NEW Having trouble with the default image colorizer, aka "artistic"? Try the "stable" one below. It generally won't produce colors that are as interesting as "artistic", but the glitches are noticeably reduced.
One of the biggest challenges when writing code to implement deep learning networks, particularly for us newbies, is getting all of the tensor (matrix and vector) dimensions to line up properly. It's really easy to lose track of tensor dimensionality in complicated expressions involving multiple tensors and tensor operations. Even when just feeding data into predefined Tensorflow network layers, we still need to get the dimensions right. When you ask for improper computations, you're going to run into some less than helpful exception messages. To help myself and other programmers debug tensor code, I built this library. TensorSensor clarifies exceptions by augmenting messages and visualizing Python code to indicate the shape of tensor variables (see figure to the right for a teaser). It works with Tensorflow, [PyTorch](https://pytorch.org/...
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations).
Python随身听-2020-10-09-技术精选
🤩Python随身听-技术精选： /TheAlgorithms/Python
👉All Algorithms implemented in Python
😎TOPICS:
python,algorithm,algorithms-implemented,algorithm-competitions,algos,sorts,searches,sorting-algorithms,education,learn,practice,community-driven,interview,hacktoberfest⭐️STARS：88537, 今日上升数↑:406
👉README：
The Algorithms - Python
All algorithms implemented in Python (for education)
These implementations are for learning purposes. They may be less efficient than the implementations in the Python standard library.
Contribution G...
地址：https://github.com/TheAlgorithms/Python
🤩Python随身听-技术精选： /eriklindernoren/ML-From-Scratch
👉Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
😎TOPICS:
machine-learning,deep-learning,deep-reinforcement-learning,machine-learning-from-scratch,data-science,data-mining,genetic-algorithm⭐️STARS：17735, 今日上升数↑:313
👉README：
Machine Learning From Scratch
About
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.
The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible
but rather to present the inner workings of them in a transparent and accessible way.
Table of Contents
+...
地址：https://github.com/eriklindernoren/ML-From-Scratch
🤩Python随身听-技术精选： /nvbn/thefuck
👉Magnificent app which corrects your previous console command.
😎TOPICS:
python,shell⭐️STARS：56332, 今日上升数↑:172
👉README：
The Fuck is a magnificent app, inspired by a @liamosaur
tweet,
that corrects errors in previous console commands.
Is The Fuck too slow? Try the experimental instant mode!
More examples:
➜ apt-get install vim
E: Could not open lock file /var/lib/dpkg/lock - open (13: Permission denied)
E: Unable to lock the administration directory (/var/lib/dpkg/), are you root?
➜ fuck
sudo apt-get install vim [enter/↑/↓/ctrl+c]
[sudo] password for nvbn:
Reading package lists... Done
...
➜ git push
fatal: The current branch master has no upstream branch.
To push the current branch and set the remote as upstream, use
➜ fuck
git push --set-upstream origin master [enter/↑/↓/ctrl+c]
Counting objects: 9, done.
...
➜ puthon
No command 'puthon' found, did you mean:
Command 'python' from package 'python-minimal' (main)
Command 'python' from package 'pyth...
地址：https://github.com/nvbn/thefuck
🤩Python随身听-技术精选： /Asabeneh/30-Days-Of-Python
👉30 days of Python programming challenge is a step by step guide to learn Python programming language in 30 days.
😎TOPICS:
30-days-of-python,python⭐️STARS：1801, 今日上升数↑:154
👉README：
🐍 30 Days Of Python
地址：https://github.com/Asabeneh/30-Days-Of-Python
🤩Python随身听-技术精选： /AlfredoSequeida/fvid
👉fvid is a project that aims to encode any file as a video using 1-bit color images to survive compression algorithms for data retrieval.
😎TOPICS:
open-source,ffmpeg,pillow,video,youtube,hacktoberfest2020,hacktoberfest⭐️STARS：156, 今日上升数↑:50
👉README：
Demonstration/Explanation Video
fvid is a project that aims to encode any file as a video using 1-bit color images
to survive compression algorithms for data retrieval.
Installation
Requires installation of FFmpeg and libmagic first, then install using pip3
Linux/macOS
pip3 install fvid
Windows
py -m pip install fvid
Usage
Encoding files as videos
Linux/OSX
fvid -i [input file] -e
Windows
py -m fvid -i [input file] -e
Retrieving data from videos
Linux/OSX
fvid -i [input video...
地址：https://github.com/AlfredoSequeida/fvid
🤩Python随身听-技术精选： /MaartenGr/BERTopic
👉Leveraging BERT and a class-based TF-IDF to create easily interpretable topics.
😎TOPICS:
bert,transformers,topic-modeling,sentence-embeddings,nlp,machine-learning⭐️STARS：168, 今日上升数↑:68
👉README：
BERTopic is a topic modeling technique that leverages BERT embeddings and c-TF-IDF to create dense clusters
allowing for easily interpretable topics whilst keeping important words in the topic descriptions.
Corresponding medium post can be found here.
Table of Contents
2.1. Sentence Transformer
2.2. UMAP + HDBSCAN
2.3. c-TF-IDF
3.1. Installation
3.2. Basic Usage
3.3. Overview
1. About the Project
Back to ToC
T...
地址：https://github.com/MaartenGr/BERTopic
🤩Python随身听-技术精选： /tensorflow/models
👉Models and examples built with TensorFlow
😎TOPICS: ``
⭐️STARS：66557, 今日上升数↑:25
👉README：
Welcome to the Model Garden for TensorFlow
The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users
can take full advantage of TensorFlow for their research and product development.
• Officially maintained, supported, and kept up to date with the latest TensorFlow 2 APIs by TensorFlow
• Reasonably optimized for fast performance while still being easy to read
• Maintained and supported by researchers
地址：https://github.com/tensorflow/models
🤩Python随身听-技术精选： /NVIDIA/NeMo
👉NeMo: a toolkit for conversational AI
😎TOPICS:
deep-learning,speech-recognition,nlp,nlp-machine-learning,neural-network⭐️STARS：1985, 今日上升数↑:13
👉README：
|status| |license| |lgtm_grade| |lgtm_alerts| |black|
.. |status| image:: http://www.repostatus.org/badges/latest/active.svg
:target: http://www.repostatus.org/#active
:alt: Project Status: Active – The project has reached a stable, usable state and is being actively developed.
.. |license| image:: https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg
:target: https://github.com/NVIDIA/NeMo/blob/master/LICENSE
:alt: NeMo core license and license for collections in this repo
.. |lgtm_grade| image:: https://img.shields.io/lgtm/grade/python/g/NVIDIA/NeMo.svg?logo=lgtm&logoWidth=18
:target: https://lgtm.com/projects/g/NVIDIA/NeMo/context:python
:alt: Language grade: Python
.. |lgtm_alerts| image:: https://img.shields.io/lgtm/alerts/g/NVIDIA/NeMo.svg?logo=lgtm&logoWidth=18
:target: https://lgtm.com/projects/g/NVIDIA/NeMo/alerts/
:alt: Total alerts
.. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black
:alt: Code st...
地址：https://github.com/NVIDIA/NeMo
🤩Python随身听-技术精选： /marblexu/PythonPlantsVsZombies
👉a simple PlantsVsZombies game
😎TOPICS: ``
⭐️STARS：1534, 今日上升数↑:26
👉README：
PythonPlantsVsZombies
A simple PlantsVsZombies game.
It's only for personal learning and noncommercial use. If this game infringes the copyright, please let me know.地址：https://github.com/marblexu/PythonPlantsVsZombies
🤩Python随身听-技术精选： /malwaredllc/byob
👉An open-source post-exploitation framework for students, researchers and developers.
😎TOPICS:
encrypted-connections,platform-independent,zero-configuration,no-dependencies,reverse-shells,antiforensics,post-exploitation⭐️STARS：5223, 今日上升数↑:28
👉README：
Questions? Join our Discord server
Disclaimer: This project should be used for authorized testing or educational purposes only.
BYOB is an open-source post-exploitation framework for students, researchers and developers. It includes features such as:
It is designed to allow students and developers to easily implement their own code and add cool new
features without having to write a C2 server or Remote Administration Tool from scratch.
This project has 2 main parts: the original console-based application (
/byob) and the web GUI (/web-gui).Web GUI
Dashboard
A control panel for your C2 server with a point-and-click interface for executing post-exploitation modules. The control panel includes an interactive map of client machines and a dashboard...
地址：https://github.com/malwaredllc/byob
🤩Python随身听-技术精选： /Pitt-CSC/Summer2021-Internships
👉Collection of Summer 2021 tech internships!
😎TOPICS:
interview-preparation,internships,hacktoberfest⭐️STARS：2131, 今日上升数↑:13
👉README：
Summer 2021 Internships ☀️👩💻
Use this repo to share and keep track of any tech-related internships. For a Google Sheet 📝 version of this repo (that remains in sync with this table) click here! For more tips on the internship process check out the Zero to Offer 📈 program here.
📫 Want weekly internship postings in your inbox? Join the internship newsletter here!
🎓 Check out our New Grad repo here.
🤗 Contribute by submitting a pull request or filling out this form! 🤗
The List 👔
地址：https://github.com/Pitt-CSC/Summer2021-Internships
🤩Python随身听-技术精选： /timoschick/pet
👉This repository contains the code for "Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference"
😎TOPICS:
nlp,python,machine-learning⭐️STARS：475, 今日上升数↑:33
👉README：
Pattern-Exploiting Training (PET)
This repository contains the code for Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference and It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. The papers introduce pattern-exploiting training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases. In low-resource settings, PET and iPET significantly outperform regular supervised training, various semi-supervised baselines and even GPT-3 despite requiring 99.9% less parameters. The iterative variant of PET (iPET) trains multiple generations of models and can even be used without any training data.
🤩Python随身听-技术精选： /keras-team/keras
👉Deep Learning for humans
😎TOPICS:
deep-learning,tensorflow,neural-networks,machine-learning,data-science,python⭐️STARS：49932, 今日上升数↑:16
👉README：
# Keras: Deep Learning for humans
Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow.
Read the documentation at Keras.io.
Multi-backend Keras and tf.keras
**Multi-backend Keras has been discontinued. At this time, we recommend that Keras us...
地址：https://github.com/keras-team/keras
🤩Python随身听-技术精选： /public-apis/public-apis
👉A collective list of free APIs for use in software and web development.
😎TOPICS: ``
⭐️STARS：97638, 今日上升数↑:61
👉README：
A collective list of free APIs for use in software and web development.
A public API for this project can be found here!
For information on contributing to this project, please see the contributing guide.
Please note a passing build status indicates all listed APIs are available since the last update. A failing build status indicates that 1 or more services may be unavailable at the moment.
Index
地址：https://github.com/public-apis/public-apis
🤩Python随身听-技术精选： /hamuchiwa/AutoRCCar
👉OpenCV Python Neural Network Autonomous RC Car
😎TOPICS: ``
⭐️STARS：2782, 今日上升数↑:18
👉README：
AutoRCCar
Python3 + OpenCV3
See self-driving in action
This project builds a self-driving RC car using Raspberry Pi, Arduino and open source software. Raspberry Pi collects inputs from a camera module and an ultrasonic sensor, and sends data to a computer wirelessly. The computer processes input images and sensor data for object detection (stop sign and traffic light) and collision avoidance respectively. A neural network model runs on computer and makes predictions for steering based on input images. Predictions are then sent to the Arduino for RC car control.
Setting up environment with Anaconda
miniconda(Python3)on your computerauto-rccarenvironment with all necessary libraries for this project地址：https://github.com/hamuchiwa/AutoRCCar
🤩Python随身听-技术精选： /facebookresearch/frankmocap
👉A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
😎TOPICS: ``
⭐️STARS：519, 今日上升数↑:17
👉README：
FrankMocap: A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
FrankMocap pursues an easy-to-use single view 3D motion capture system developed by Facebook AI Research (FAIR). FrankMocap provides state-of-the-art 3D pose estimation outputs for body, hand, and body+hands in a single system. The core objective of FrankMocap is to democratize the 3D human pose estimation technology, enabling anyone (researchers, engineers, developers, artists, and others) can easily obtain 3D motion capture outputs from videos and images.
Key Features
地址：https://github.com/facebookresearch/frankmocap
🤩Python随身听-技术精选： /ageitgey/face_recognition
👉The world's simplest facial recognition api for Python and the command line
😎TOPICS:
machine-learning,face-detection,face-recognition,python⭐️STARS：36556, 今日上升数↑:23
👉README：
Face Recognition
You can also read a translated version of this file in Chinese 简体中文版 or in Korean 한국어 or in Japanese 日本語.
Recognize and manipulate faces from Python or from the command line with
the world's simplest face recognition library.
Built using dlib's state-of-the-art face recognition
built with deep learning. The model has an accuracy of 99.38% on the
Labeled Faces in the Wild benchmark.
This also provides a simple
face_recognitioncommand line tool that letsyou do face recognition on a folder of images from the command line!
Features
Find faces in pictures
Find all the faces that appear in a picture:
import face_recognition
image = face_recognition.load_image_file("your_file...
地址：https://github.com/ageitgey/face_recognition
🤩Python随身听-技术精选： /rusty1s/pytorch_geometric
👉Geometric Deep Learning Extension Library for PyTorch
😎TOPICS:
pytorch,geometric-deep-learning,graph-neural-networks⭐️STARS：9081, 今日上升数↑:13
👉README：
🤩Python随身听-技术精选： /scikit-learn/scikit-learn
👉scikit-learn: machine learning in Python
😎TOPICS:
machine-learning,python,statistics,data-science,data-analysis⭐️STARS：42531, 今日上升数↑:14
👉README：
.. -- mode: rst --
|Azure|_ |Travis|_ |Codecov|_ |CircleCI|_ |PythonVersion|_ |PyPi|_ |DOI|_
.. |Azure| image:: https://dev.azure.com/scikit-learn/scikit-learn/_apis/build/status/scikit-learn.scikit-learn?branchName=master
.. _Azure: https://dev.azure.com/scikit-learn/scikit-learn/_build/latest?definitionId=1&branchName=master
.. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.svg?branch=master
.. _Travis: https://travis-ci.org/scikit-learn/scikit-learn
.. |Codecov| image:: https://codecov.io/github/scikit-learn/scikit-learn/badge.svg?branch=master&service=github
.. _Codecov: https://codecov.io/github/scikit-learn/scikit-learn?branch=master
.. |CircleCI| image:: https://circleci.com/gh/scikit-learn/scikit-learn/tree/master.svg?style=shield&circle-token=:circle-token
.. _CircleCI: https://circleci.com/gh/scikit-learn/scikit-learn
.. |PythonVersion| image:: https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-blue
.. _PythonVersion: https://img.shields.io/badge/python-3.6...
地址：https://github.com/scikit-learn/scikit-learn
🤩Python随身听-技术精选： /facebookresearch/ParlAI
👉A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
😎TOPICS: ``
⭐️STARS：6663, 今日上升数↑:11
👉README：
🤩Python随身听-技术精选： /eladrich/pixel2style2pixel
👉Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation"
😎TOPICS:
image-translation,stylegan,generative-adversarial-network,stylegan-encoder⭐️STARS：185, 今日上升数↑:67
👉README：
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
地址：https://github.com/eladrich/pixel2style2pixel
🤩Python随身听-技术精选： /jantic/DeOldify
👉A Deep Learning based project for colorizing and restoring old images (and video!)
😎TOPICS: ``
⭐️STARS：11699, 今日上升数↑:12
👉README：
DeOldify
Quick Start: The easiest way to colorize images using DeOldify (for free!) is here: DeOldify Image Colorization on DeepAI
The most advanced version of DeOldify image colorization is available here, exclusively. Try a few images for free! MyHeritiage In Color
Image (artistic)
|
Video
NEW Having trouble with the default image colorizer, aka "artistic"? Try the "stable" one below. It generally won't produce colors that are as interesting as "artistic", but the glitches are noticeably reduced.
Image (stable) [<img src="https://co...
地址：https://github.com/jantic/DeOldify
🤩Python随身听-技术精选： /NLP-LOVE/ML-NLP
👉此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
😎TOPICS:
nlp,machine-learning,deep-learning⭐️STARS：6531, 今日上升数↑:19
👉README：
项目介绍
目录
地址：https://github.com/NLP-LOVE/ML-NLP
🤩Python随身听-技术精选： /Atcold/pytorch-Deep-Learning
👉Deep Learning (with PyTorch)
😎TOPICS:
jupyter-notebook,pytorch,deep-learning,neural-nets⭐️STARS：3012, 今日上升数↑:116
👉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： /parrt/tensor-sensor
👉The goal of this library is to generate more helpful exception messages for numpy/pytorch matrix algebra expressions.
😎TOPICS:
numpy,pytorch,matrix,vector,debugging,tracing⭐️STARS：188, 今日上升数↑:31
👉README：
Tensor Sensor
地址：https://github.com/parrt/tensor-sensor
🤩Python随身听-技术精选： /slundberg/shap
👉A game theoretic approach to explain the output of any machine learning model.
😎TOPICS:
interpretability,machine-learning,deep-learning,gradient-boosting,shap,shapley,explainability⭐️STARS：10395, 今日上升数↑:15
👉README：
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations).
Install
Shap can be installed from either ...
地址：https://github.com/slundberg/shap
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