#Kaggle
Showing 60 of 139 repositories tagged #kaggle, ranked by stars
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Generate audiobooks from e-books, voice cloning & 1158+ languages!
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
深度学习入门教程, 优秀文章, Deep Learning Tutorial
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Interview = 简历指南 + 算法题 + 八股文 + 源码分析
🏅 Collection of Kaggle Solutions and Ideas 🏅
数据挖掘、计算机视觉、自然语言处理、推荐系统竞赛知识、代码、思路
A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.
免费学代码系列：小白python入门、数据分析data analyst、机器学习machine learning、深度学习deep learning、kaggle实战
PyTorch extensions for fast R&D prototyping and Kaggle farming
MLBox is a powerful Automated Machine Learning python library.
A searchable compilation of Kaggle past solutions
Fast and customizable framework for automatic ML model creation (AutoML)
LAMA - automatic model creation framework
Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).
key Deep Learning engineering tricks in recsys
Code for Kaggle Data Science Competitions
Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R
Một cuốn sách về Học Sâu đề cập đến nhiều framework phổ biến, được sử dụng trên 300 trường Đại học từ 55 đất nước bao gồm MIT, Stanford, Harvard, và Cambridge.
Kaggle datascience bowl 2017
Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN (GRU and LSTM) and Word Embeddings on Tensorflow.
AutoX is an efficient automl tool, which is mainly aimed at data mining tasks with tabular data.
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
The First Place Solution of Kaggle iMaterialist (Fashion) 2019 at FGVC6
Open solution to the Home Credit Default Risk challenge :house_with_garden:
[UNMAINTAINED] Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml
Open solution to the Mapping Challenge :earth_americas:
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
Pytorch starter kit for Kaggle competitions
从零基础开始机器学习之旅
Your one-click scientific research lab for Opencode - with seamless .ipynb and REPL integration
Grandmaster in MachineHack (3rd Rank Best) | Top 70 in AnalyticsVidya & Zindi | Expert at Kaggle | Hack AI
Papers and datasets for Vibration Analysis
Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks. It's designed for people who want to learn machine learning.
数据分析，挖掘建模。
Speech commands recognition with PyTorch | Kaggle 10th place solution in TensorFlow Speech Recognition Challenge
The Java implementation of Dive into Deep Learning (D2L.ai)
Kaggle을 처음 접하는 사람들을 위한 문서
Practices on data analysis including: cleaning, visualization and EDA on different datasets using Python, SQL, Power BI, etc.
Kaggle Kernel for House Prices competition https://www.kaggle.com/massquantity/all-you-need-is-pca-lb-0-11421-top-4
《머신러닝·딥러닝 문제해결 전략》
Open solution to the Data Science Bowl 2018
Open solution to the Toxic Comment Classification Challenge
Distributed XGBoost on Ray
Solution of the Titanic Kaggle competition
Fast and customizable framework for automatic and quick Causal Inference in Python
🤖 AI Agent-driven Kaggle competition workflow. Battle-tested patterns for score stabilization, submission troubleshooting, kernel workflows, and spec-driven development.
A concise resource repository for machine learning
The Google Advanced Data Analytics Certificate contains information on how to use machine learning, predictive modeling, and experimental design to collect and analyze large amounts of data, and prepare for jobs like Senior Data Analyst and Junior Data Scientist.
This repository contains notes and projects of Data scientist track from dataquest course work.
Tensorflow implementation : U-net and FCN with global convolution
Spark 2.0 Python Machine Learning examples
✌️Y Combinator directory scraper
This repository is a case study, analysis and visualization of COVID-19 Pandemic spread along with prediction models.
1st place solution of RSNA Screening Mammography Breast Cancer Detection competition on Kaggle: https://www.kaggle.com/competitions/rsna-breast-cancer-detection
Data science Python notebooks—a collection of Jupyter notebooks on machine learning, deep learning, statistical inference, data analysis and visualization.