--- tags: machine learning --- Machine Learning 學習資源 === e-Book --- + [Pattern Recognition and Machine Learning (中譯)](https://www.gitbook.com/book/mqshen/prml/details) + 一本詳細介紹ML相關數學理論的書 + [手把手教你用tensorflow跟keras 中文](http://tensorflowkeras.blogspot.tw/?max-results=5) + 一本很簡單的書，看完之後超快做得出來但會不太懂原理 + [Neural Networks and Deep Learning - Michael Nielsen](http://neuralnetworksanddeeplearning.com/) + 講類神經和DL相當推薦的一本線上書 + [Computational Intelligence: Concepts to Implementations](http://www.computelligence.org/issue/CICI/CICI.html) + 製造所IMS課程用書，據說是聖經級的書，內容除了NN以外還有EC和Fuzzy + [An Introduction to Statistical Learning - with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) + IMS用書 + [UCB Crash Course Series:The official blog of Machine Learning @ Berkeley](https://ml.berkeley.edu/blog/tutorials/) + 虽然是blog但是相当于online book了。完整详细，从基础开始。 + [Deep Learning - Ian Goodfellow and Yoshua Bengio and Aaron Courville](http://www.deeplearningbook.org/) + 大神們寫的一本範圍廣泛的聖經級書籍，有數學理論、CNN、計算機視覺、自然語言處理、遊戲上的應用等等。另外也有[簡中開源版](https://exacity.github.io/deeplearningbook-chinese/)，沒圖片就去 github 載 PDF + [Data Science and Robots - Brandon Rohrer](https://brohrer.github.io/blog.html), [中譯](https://brohrer.github.io/blog.html) + Facebook 的現役資料科學家 Brandon Rohrer 所撰寫的部落格 Data Science and Robots，內容淺白而且有蠻多技術以外的議題探討 + [Mechine Learning Yearning - Andrew Ng 吳恩達](https://mlyearning.us6.list-manage.com/track/click?u=dc3a7ef4d750c0abfc19202a3&id=a565765c04&e=0246bbc486) + 吳恩達寫的新書(已完成) + [Convex Optimization – Boyd and Vandenberghe](http://web.stanford.edu/~boyd/cvxbook/) + Convex Optimization 為機器學習的低層數學 + [神经网络与深度学习 - 邱锡鹏](https://nndl.github.io/) + NN,DL入門書 作者是中文NLP項目FudanNLP負責人 Video --- + [Deep learning - 3Blue1Brown - Youtube](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) + 一個數學科普頻道有關DL的系列影片 + [CS231n lecture- Youtube](https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC&index=1&t=0s) + Stanford Computer Vision Lab推出的一門適合入門DL的課 有簡中字幕 MOOC --- + [deeplearn.ai Andrew Ng(前百度資料科學家大頭)](https://www.coursera.org/specializations/deep-learning) + 目前的課都修完了，他會教你不用framewrok刻ＮＮmodel，學完對原理比較懂 + [台大教授 李宏毅 深度學習](https://www.youtube.com/channel/UC2ggjtuuWvxrHHHiaDH1dlQ) + 圖像辨識，語音辨識，GAN + [台大教授 林軒田（IBM資料科學家）機器學習](https://www.youtube.com/channel/UC9Wi1Ias8t4u1OosYnHhi0Q) + 比較重視結構化資料的分析方式 ex.svm + [Reinforcement Learning](https://classroom.udacity.com/courses/ud600) + Udacity與GaTech合作開的RL課程 + [MIT 6.S099: Artificial General Intelligence](https://agi.mit.edu/) + MIT的AGI課程 + [UCL Course on RL](http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html) + UCL的RL課程 + [MIT 6.S191 Introduction to Deep Learning](http://introtodeeplearning.com/) + MIT的DL的課程 + [Udacity: Deep Learning by google](https://www.udacity.com/course/deep-learning--ud730) + [Google Machine learning Crash Course](https://developers.google.com/machine-learning/crash-course/) + Google的TF課程 + [UvA Deep Learning Course](https://uvadlc.github.io/) + 荷蘭阿姆斯特丹大學的DL課程 數學課 --- + [Khan Academy - Linear Algebra](https://www.khanacademy.org/math/linear-algebra) + 線性代數 + [Khan Academy - Statistics and probability](https://www.khanacademy.org/math/statistics-probability) + 機率與統計 + [Khan Academy - Precalculus](https://www.khanacademy.org/math/precalculus) + (複習用)國高中程度基礎數學 + [Khan Academy - Calculus](https://www.khanacademy.org/math/calculus-home) + (複習用)高中程度的微積分 + [Khan Academy - Algebra](https://www.khanacademy.org/math/algebra-home) + (複習用)高中程度的代數 其他未整理的東西 --- + [Luis Serrano's Neural Network Series (REALLY GOOD)](https://www.youtube.com/watch?v=UNmqTiOnRfg) + [Giant_Neural_Network's Neural Network Series (REALLY GOOD)](https://www.youtube.com/watch?v=ZzWaow1Rvho) + [Macheads101's Neural Network series](https://www.youtube.com/watch?v=OypPjvm4kiA) + [3Blue1Brown's Neural Network Series](https://www.youtube.com/watch?v=aircAruvnKk) + [Hugo's Neural Network Series](http://www.mooc.ai/course/300) + [James Mccaffrey Talk (A BIT OUTDATED INFORMATION BUT STILL A FUN ONE)](https://www.youtube.com/watch?v=-zT1Zi_ukSk) + [What are some good resources to learn about optimization? - Quora](https://www.quora.com/What-are-some-good-resources-to-learn-about-optimization) + [Deep Learning Papers for Fish](https://github.com/scofield7419/Deep-Learning-Papers-for-Fish)