kaggle

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

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.

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

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

Jupyter Notebooks for Ollama integration

Distill teacher chains-of-thought into a LoRA adapter via a strict boxed-answer format contract + two-phase Train→Nudge (silver-medal NVIDIA Nemotron reasoning recipe, as a tested library).

The most complete and actively updated FIFA World Cup 2026 dataset on Kaggle. A clean, authentic, relational dataset covering the entire tournament (June 11 – July 19, 2026) with the first-ever 48-team format. Includes real match results updated daily, 1,248 players across all 48 squads, expected goals (xG), minute-by-minute match events.

Google QUEST Q&A Labeling Kaggle Competition 6th Place Solution

Developed a sophisticated machine learning model capable of generating diverse interview questions aligned with specific topics, ensuring depth of conversation. Integrated advanced Natural Language Processing (NLP) algorithms to analyse spoken responses, identifying grammatical errors & offering accurate corrections after the interview.

A1111, ComfyUI, Forge, Forge-Classic/Neo, ReForge, SD-UX - One NoteBook for Google Colab & Kaggle

Stable-Diffusion-WebUI - NoteBook | Colab (Pro/Free), Kaggle.