NotebooksLearn Python for free using open-source notebooks in Hebrew.
Stars: ✭ 877 (-80.15%)
Python AwesomeLearn Python, Easy to learn, Awesome
Stars: ✭ 219 (-95.04%)
Python Note《Python 学习手册》(第四版 + 第五版)笔记
Stars: ✭ 74 (-98.33%)
VirgilioVirgilio is developed and maintained by these awesome people.
You can email us virgilio.datascience (at) gmail.com or join the Discord chat.
Stars: ✭ 13,200 (+198.78%)
Python referenceUseful functions, tutorials, and other Python-related things
Stars: ✭ 3,125 (-29.27%)
Data Science ProjectsDataScience projects for learning : Kaggle challenges, Object Recognition, Parsing, etc.
Stars: ✭ 361 (-91.83%)
Tts🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Stars: ✭ 305 (-93.1%)
Nerf plNeRF (Neural Radiance Fields) and NeRF in the Wild using pytorch-lightning
Stars: ✭ 362 (-91.81%)
Skope Rulesmachine learning with logical rules in Python
Stars: ✭ 362 (-91.81%)
Sagemaker DeploymentCode and associated files for the deploying ML models within AWS SageMaker
Stars: ✭ 361 (-91.83%)
Python CourseTutorial and introduction into programming with Python for the humanities and social sciences
Stars: ✭ 370 (-91.63%)
D2l PytorchThis project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
Stars: ✭ 3,810 (-13.76%)
Kaggle titanicthe data and ipython notebook of my attempt to solve the kaggle titanic problem
Stars: ✭ 363 (-91.78%)
Machinelearning Deeplearning Nlp Leetcode Statisticallearningmethod Tensorflow最近在学习机器学习,深度学习,自然语言处理,统计学习方法等知识,理论学习主要根据readme的链接,在学习理论的同时,决定自己将学习的相关算法用Python实现一遍,并结合GitHub上相关大牛的代码进行改进,本项目会不断的更新相关算法,欢迎star,fork和关注。 主要包括: 1.吴恩达Andrew Ng老师的机器学习课程作业个人笔记 Python实现, 2.deeplearning.ai(吴恩达老师的深度学习课程笔记及资源) Python实现, 3.李航《统计学习方法》 Python代码实现, 4.自然语言处理NLP 牛津大学xDeepMind Python代码实现, 5.LeetCode刷题,题析,分析心得笔记 Java和Python代码实现, 6.TensorFlow人工智能实践代码笔记 北京大学曹健老师课程和TensorFlow:实战Google深度学习框架(第二版) Python代码实现, 附带一些个人心得和笔记。GitHub上有很多机器学习课程的代码资源,我也准备自己实现一下,后续会更新笔记,代码和百度云网盘链接。 这个项目主要是学习算法的,并且会不断更新相关资源和代码,欢迎关注,star,fork! Min's blog 欢迎访问我的博客主页! (Welcome to my blog website !)https://liweimin1996.github.io/
Stars: ✭ 359 (-91.87%)
ArticlesA repository for the source code, notebooks, data, files, and other assets used in the data science and machine learning articles on LearnDataSci
Stars: ✭ 350 (-92.08%)
Deepstream python appsA project demonstrating use of Python for DeepStream sample apps given as a part of SDK (that are currently in C,C++).
Stars: ✭ 359 (-91.87%)
Tensorflow chessbotPredict chessboard FEN layouts from images using TensorFlow
Stars: ✭ 362 (-91.81%)
Covid19pt Data😷️🇵🇹 Dados relativos à pandemia COVID-19 em Portugal
Stars: ✭ 362 (-91.81%)
Mv3dMulti-View 3D Object Detection Network for Autonomous Driving
Stars: ✭ 362 (-91.81%)
Intro programmingA set of IPython notebooks and learning resources for an Introduction to Programming class, focusing on Python.
Stars: ✭ 366 (-91.72%)
IvpidPython PID Controller
Stars: ✭ 361 (-91.83%)
SdvSynthetic Data Generation for tabular, relational and time series data.
Stars: ✭ 360 (-91.85%)
Fbrs interactive segmentation[CVPR2020] f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation https://arxiv.org/abs/2001.10331
Stars: ✭ 366 (-91.72%)
Deep learning coronavirus cureUsing deep learning to generate novel molecules as candidates for binding with coronavirus protease
Stars: ✭ 361 (-91.83%)
Optunityoptimization routines for hyperparameter tuning
Stars: ✭ 362 (-91.81%)
Data ScienceCollection of useful data science topics along with code and articles
Stars: ✭ 315 (-92.87%)
PyfolioPortfolio and risk analytics in Python
Stars: ✭ 4,167 (-5.68%)
Lagomlagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
Stars: ✭ 364 (-91.76%)
GraphgymPlatform for designing and evaluating Graph Neural Networks (GNN)
Stars: ✭ 344 (-92.21%)
Quantitative NotebooksEducational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
Stars: ✭ 356 (-91.94%)
AudionotebooksCollection of notebooks and scripts related to audio processing and machine learning.
Stars: ✭ 366 (-91.72%)
Cs229 ml🍟 Stanford CS229: Machine Learning
Stars: ✭ 364 (-91.76%)
Predicting PovertyCombining satellite imagery and machine learning to predict poverty
Stars: ✭ 358 (-91.9%)
SpotmicroaiSpotMicro AI - How to build a self-learning Robot
Stars: ✭ 357 (-91.92%)
Integrated GradientsAttributing predictions made by the Inception network using the Integrated Gradients method
Stars: ✭ 365 (-91.74%)
Workshopslecture notes for cyberwizard workshops
Stars: ✭ 363 (-91.78%)