Machine Learning Mastery

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Making developers awesome at machine learning.

所在地:
加拿大
语言:
zh
收录时间:
2025-09-11
Machine Learning MasteryMachine Learning Mastery

AI开放平台

Making developers awesome at machine learning.

Machine Learning Mastery 提供从入门到进阶的机器学习和深度学习教程,覆盖算法实现、数据处理、时间序列预测、自然语言处理和深度学习框架应用等核心内容。

网站重点功能或内容:

  1. 免费资源与电子书(实用教程、代码示例、项目指导)

  2. 深度学习框架应用(Keras、PyTorch、Hugging Face Transformers)

  3. 数据处理与分析(数据准备、特征工程、统计、优化)

  4. 教程与指南(从入门到高级机器学习与深度学习)

  5. 时间序列与自然语言处理(LSTMs、NLP、生成对抗网络)

  6. 算法实现(Python、R 等语言实现机器学习算法)

功能模块 详细内容 重点说明
导航 (Navigation) Get Started, Blog, Topics, Attention, Better Deep Learning, Calculus, ChatGPT, Code Algorithms, Computer Vision, Data Preparation, Deep Learning (Keras), Deep Learning with PyTorch, Ensemble Learning, Foundations of Data Science, GANs, Hugging Face Transformers, Neural Net Time Series, NLP (Text), Imbalanced Learning, Intermediate Data Science, Intro to Time Series, Intro to Algorithms, Linear Algebra, LSTMs, OpenCV, Optimization, Probability, Python (scikit-learn), Python for Machine Learning, R (caret), Stable Diffusion, Statistics, Weka (no code), XGBoost 提供网站整体结构导航和各类主题入口,方便快速访问教程和资源
eBooks & 免费资源 Send Me the Free eBook!, Get the Free eBook! 提供免费下载的电子书,涵盖机器学习和深度学习实用教程
入门指南 How to get started, Understanding algorithms, Time series forecasting, How to install everything, Your first complete project, Your first neural network, Master Machine Learning Algos, Machine Learning With Python, Deep Learning With Python 系统性入门教程,涵盖从安装环境到完成第一个项目的完整流程
法律与服务条款 Privacy, Disclaimer, Terms, Sitemap, ADVERTISE WITH US, Do not sell or share my personal information, Terms of Content Use 网站隐私政策、免责声明及内容使用条款,保证合法合规
社交与联系 LinkedIn, Facebook, Twitter, RSS Feed, Mail, Contact 提供社交媒体链接及联系方式,便于获取最新资讯和交流学习经验
作者与贡献者 Jason Brownlee, Ph.D., Adrian Tam, Ph.D., Estephania Cristina, Ph.D., Kanwal Mehreen 提供教程和文章的专业作者信息,保证内容权威性
精选文章与技巧 7 Scikit-learn Tricks for Optimized Cross-Validation, A Gentle Introduction to Batch Normalization, Small Language Models are the Future of Agentic AI, 10 Python One-Liners Every Machine Learning Practitioner Should Know, 3 Ways to Speed Up and Improve Your XGBoost Models, 5 Key Ways LLMs Can Supercharge Your Machine Learning Workflow 实用教程与技巧文章,协助学习者提升机器学习实践能力

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