ZIP斯坦福cs229课程笔记小抄 82.94MB

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资源文件列表:

stanford-cs-229-machine-learning.zip 大约有174个文件
  1. stanford-cs-229-machine-learning/
  2. stanford-cs-229-machine-learning/vi/
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  8. stanford-cs-229-machine-learning/zh/
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  50. stanford-cs-229-machine-learning/zh/无监督学习/
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资源介绍:

斯坦福大学著名的cs229机器学习课程可谓无人不知无人不晓，但其丰富庞杂的内容有时候也令人望而却步。 资源里整理了全课程中最重要的概念重点，做成了高度凝练的笔记小抄，一方面可以作为学习者的快速复习资料，另一方面也方便那些没有时间深入了解课程但希望快速掌握核心知识点的专业人士。这份笔记小抄涵盖了以下几个方面： 基础概念：包括机器学习的定义、分类、以及监督学习、无监督学习、强化学习等基本类型。 主要算法：从线性回归、逻辑回归到决策树、随机森林，再到支持向量机和神经网络，每个算法的原理、优缺点和应用场景都进行了简要介绍。 模型评估：介绍了交叉验证、偏差-方差权衡、ROC曲线等模型评估方法，帮助学习者理解如何评估和选择模型。 优化技术：包括梯度下降、随机梯度下降等优化算法，以及正则化技术在防止过拟合中的应用。 特征工程：讨论了特征选择、特征提取和特征构造等关键步骤，以及它们在提高模型性能中的作用。 深度学习：特别强调了深度学习的重要性，包括卷积神经网络(CNN)、循环神经网络(RNN)、长短期记忆网络(LSTM)等高级模型。
# Machine Learning cheatsheets for Stanford's CS 229 Available in [العربية](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/ar) - [English](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/en) - [Español](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/es) - [فارسی](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/fa) - [Français](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/fr) - [한국어](https://stanford.edu/~shervine/l/ko/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks) - [Português](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/pt) - [Türkçe](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/tr) - [Tiếng Việt](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/vi) - [简中](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/zh) - [繁中](https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/zh-tw) ## Goal This repository aims at summing up in the same place all the important notions that are covered in Stanford's CS 229 Machine Learning course, and include: - **Refreshers** in related topics that highlight the key points of the **prerequisites of the course**. - **Cheatsheets for each machine learning field**, as well as another dedicated to tips and tricks to have in mind when training a model. - All elements of the above combined in an **ultimate compilation of concepts**, to have with you at all times! ## Content #### VIP Cheatsheets |<a href="https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf"><img src="https://stanford.edu/~shervine/teaching/cs-229/illustrations/cover/en-001.png?" alt="Illustration" width="220px"/></a>|<a href="https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf"><img src="https://stanford.edu/~shervine/teaching/cs-229/illustrations/cover/en-002.png" alt="Illustration" width="220px"/></a>|<a href="https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf"><img src="https://stanford.edu/~shervine/teaching/cs-229/illustrations/cover/en-003.png" alt="Illustration" width="220px"/></a>|<a href="https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf"><img src="https://stanford.edu/~shervine/teaching/cs-229/illustrations/cover/en-004.png" alt="Illustration" width="220px"/></a>| |:--:|:--:|:--:|:--:| |Supervised Learning|Unsupervised Learning|Deep Learning|Tips and tricks| #### VIP Refreshers |<a href="https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf"><img src="https://stanford.edu/~shervine/teaching/cs-229/illustrations/cover/en-005.png" alt="Illustration" width="220px"/></a>|<a href="https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf"><img src="https://stanford.edu/~shervine/teaching/cs-229/illustrations/cover/en-006.png#1" alt="Illustration" width="220px"/></a>| |:--:|:--:| |Probabilities and Statistics|Algebra and Calculus| #### Super VIP Cheatsheet |<a href="https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf"><img src="https://stanford.edu/~shervine/teaching/cs-229/illustrations/cover/en-007.png" alt="Illustration" width="400px"/></a>| |:--:| |All the above gathered in one place| ## Website This material is also available on a dedicated [website](https://stanford.edu/~shervine/teaching/cs-229), so that you can enjoy reading it from any device. ## Translation Would you like to see these cheatsheets in your native language? You can help us translating them on [this dedicated repo](https://github.com/shervinea/cheatsheet-translation)! ## Authors [Afshine Amidi](https://twitter.com/afshinea) (Ecole Centrale Paris, MIT) and [Shervine Amidi](https://twitter.com/shervinea) (Ecole Centrale Paris, Stanford University)
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