Date Title Description Post Links
2018-03-09 AI Student Kits Learn AI theory and follow hands-on exercises with our free courses for software developers, data scientists, and students. These lessons cover AI topics and explore tools and optimized libraries that take advantage of Intel® processors in personal computers and server workstations. MORE
2018-03-09 Neural Network Libraries by Sony An open source software to make research, developement and implementation of neural network more efficient MORE
2018-03-09 How to build a deep learning model in 15 minutes An open source framework for configuring, buildin, deploying and maintaining deep learning models in Python MORE
2018-02-27 Top 20 Python AI and Machine Learning Open Source Projects We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors. MORE
2018-02-25 TensorFlow: Building Feed-Forward Neural Networks Step-by-Step This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details. ... MORE
2018-02-21 Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch. ... MORE
2018-02-20 Deep learning for biology A popular artificial-intelligence method provides a powerful tool for surveying and classifying biological data. But for the uninitiated, the technology poses significant difficulties. ... MORE
2018-02-18 Logistic Regression: A Concise Technical Overview Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well. ... MORE
2018-02-18 New Deep Learning Techniques (Schedule) New Deep Learning Techniques (Schedule)... MORE
2018-02-15 Announcing Tensor Comprehensions Today, Facebook AI Research (FAIR) is announcing the release of Tensor Comprehensions, a C++ library and mathematical language that helps bridge the gap between researchers, who communicate in terms of mathematical operations, and engineers focusing on the practical needs of running large-scale models on various hardware backends. The main differentiating feature of Tensor Comprehensions is that it represents a unique take on Just-In-Time compilation to produce the high-performance codes that the machine learning community needs, automatically and on-demand. ... MORE
2018-01-31 7 Steps to Mastering Deep Learning with Keras Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible. ... MORE
2018-01-31 Automated Text Classification Using Machine Learning In this post, we talk about the technology, applications, customization, and segmentation related to our automated text classification API. ... MORE
2017-08-18 附資源與完整指導!帶你從零開始掌握 Python 機器學習 【我們為什麼挑選這篇文章】我是一個毫無程式基礎的文組生,一直都對機器學習很有興趣卻不知道怎麼入門。
前幾天真的是意外在中國網站上發現這篇文章,進而開始聽 Tom Mitchell 的線上課程,結果很意外的我居然就停不下來的一直聽下去,他的英語發音清晰,表達又簡單明瞭,讓我非常喜歡!很期待慢慢發掘這篇文章中的學習資源,也很推薦給大家!(責任編輯:劉庭瑋)...


Date Title Youtube Links
2019-12-04 從0開始學AI系列影片 MORE
2018-02-11 Machine Learning Foundations, NTU, Pro. Hsuan-Tien Lin MORE
2018-02-27 Machine Learning (Hung-yi Lee, NTU) MORE
2018-02-27 Advanced Topics in Deep Learning (Hung-yi Lee, NTU) MORE
2018-02-27 Introduction of Generative Adversarial Network (GAN) MORE
2018-02-27 Generative Adversarial Network(GAN) (Hung-yi Lee, NTU) MORE
2018-02-27 Improved Generative Adversarial Network (Hung-yi, Lee, NTU) MORE
2018-02-27 GANs from A to Z MORE


Date Title Website Links
2018-02-11 Machine/Deep Learning, NTU, Prof. Hung-yi Lee MORE
2018-02-11 Deep Learning, NTHU, Prof. Shan-Hung Wu MORE


Date Title Tutorial Links
2018-03-08 tutorialspoint - Python Tutorial MORE
2018-03-08 Learn Python - Free Interactive Python Tutorial MORE
2018-02-11 Cloudera Tutorial MORE
2018-02-11 Cloudera Developer MORE


Date Tool Name Description Tool Links
2018-02-27 Weka Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. MORE
2018-02-27 scikit-learn scikit-learn: Machine Learning in Python
  • Simple and efficient tools for data mining and data analy
  • Accessible to everybody, and reusable in various contexts
  • Built on NumPy, SciPy, and matplotlib
  • Open source, commercially usable - BSD license


  1. R. Agrawal and S. Ieong. Aggregating web offers to determine product prices. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 435–443, 2012.
  2. L. J. Chen, Y. H. Ho, H. H. Hsieh, S. T. Huang, H. C. Lee, and S. Mahajan. Adf: an anomaly detection framework for large-scale pm2.5 sensing systems. IEEE Internet of Things Journal, PP(99):1–1, 2017.
  3. K. Greff, R. K. Srivastava, J. Koutn’?k, B. R. Steunebrink, and J. Schmidhuber. LSTM: A search space odyssey. CoRR, abs/1503.04069, 2015.
  4. P. Haider, L. Chiarandini, and U. Brefeld. Discriminative clustering for market segmentation. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 417–425, 2012.
  5. K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV ’15, pages 1026–1034, Washington, DC, USA, 2015. IEEE Computer Society.
  6. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Comput., 9(8):1735–1780, Nov. 1997.
  7. J.-W. Huang, S.-C. Lin, and M.-S. Chen. DPSP: Distributed progressive sequential pattern mining on the cloud. In Proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 27–34, 2010.
  8. A. Kurt and A. B. Oktay. Forecasting air pollutant indicator levels with geographic models 3days in advance using neural networks. Expert Systems with Applications, 37(12):7986–7992, 2010.
  9. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, pages 2278–2324, 1998.
  10. M.-Y. Lin and S.-Y. Lee. Incremental update on sequential patterns in large databases by implicit merging and efficient counting. Information System, 29(5):385–404, 2004.
  11. X.-M. Liu, J. Biagioni, J. Eriksson, Y. Wang, G. Forman, and Y.-M. Zhu. Mining largescale, sparse GPS traces for map inference: Comparison of approaches. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 669–677, 2012.
  12. F. Masseglia, P. Poncelet, and M. Teisseire. Incremental mining of sequential patterns in large databases. Data and Knowledge Engineering, 46(1):97–121, 2003.
  13. N. Moustafa, G. Creech, E. Sitnikova, and M. Keshk. Collaborative anomaly detection framework for handling big data of cloud computing. CoRR, abs/1711.02829, 2017.
  14. S. Nguyen and M. Orlowska. Improvements of INCSPAN: Incremental mining of sequential patterns in large database. In Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 442–451, 2005.
  15. P.-W. Soh, K.-H. Chen, J.-W. Huang, and H.-J. Chu. Spatial-temporal pattern analysis and prediction of air quality in taiwan. In proceedings of the 2017 International Conference on Ubi-Media Computing(UMedia), 2017.
  16. Y. Zheng, F. Liu, and H.-P. Hsieh. U-air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1436–1444. ACM, 2013.
  17. Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, and T. Li. Forecasting fine-grained air quality based on big data. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, pages 2267–2276, New York, NY, USA, 2015. ACM.

Python Self-Learning

Date Title Description Post Links
Computer Science / IT 的面試題庫 收錄了各種難度的程式題目,可以不斷的練習寫python。 MORE
CodeCademy -- Python 可以從零開始學習Python語言,裡面有一系列的教程。 MORE
Learn Python -- 基礎學習python 適合從基礎開始學習python,每個主題都有範例展示。 MORE
Python語法兩分鐘系列 透過只有2分鐘的短片來介紹python的每一個小主題。 MORE
你所不知道的python標準函式庫用法 一個網誌網站,裡面有為一些函式庫說明使用方法的實用文章。 MORE
Learn Python The Hard Way 透過一步一步的引導來帶你學會python語言。 MORE
快速Python入門 用一個網站頁面快速讓你認識python語言及重要指令。 MORE
Pandas基礎教學 Pandas是python的一個lib,網站裡面介紹了從Pandas的安裝到基礎使用方法。 MORE
Learn python in few minutes 有基本的範例教學,適合有程式基礎的人。有些概念雖然沒有詳細說明,但範例後的註解都有關鍵字,可以依據這些關鍵字搜尋更詳細的相關資料。 MORE
Google Developer Python Course 由Google開發者免費教你Python的課程。 MORE
Beautiful Soup Beautiful Soup is a Python library for pulling data out of HTML and XML files. This documentation will introduce the Beautiful Soup MORE
給初學者的 Python 網頁爬蟲與資料 由一系列的文章教你怎麼用python進行網頁爬蟲 MORE
莫帆PYTHON 裡面有各種關於python的相關學習知識,從基礎教程到應用工具都有。 MORE
一小時的Python入門 從環境安裝到基本語法教學,讓你在一小時內就學會python。 MORE MORE
用Python自學資料科學與機器學習入門實戰 一個還不錯的自學網站,提供給大家參考,內容淺顯易懂能讓一開始觸碰Python領域的人了解機器學習。 MORE
Python 快速入門 首先,他先從Python的特性與應用開始介紹,讓大家了解在眾多語言當中,Python的優勢在哪裡。接著在介紹他的一些程式語法,讓入學者可以快速入門! MORE
Improve Your Python: Python Classes and Object Oriented Programming This article will give you a brief understanding of a python class, how you should define and use it. The article also mentioned ideas of abstract classes and inheritance, which are very important ideas of object oriented programming design technique. MORE
Python Code Examples 網站內有一些Python的範例程式,可以做為參考。 MORE
DataCamp 在這個網站上有許多關於資料科學的免費課程,其中的 Intro to Python for Data Science 簡潔明瞭的說明Python的基本概念與常用的function與method,也介紹了Numpy這個處理運算的package以及它的基本操作。這個課程分成許多小段落,每一段落一開始會有一段教學影片,接著是程式編寫的練習,或是觀念的選擇題,可以邊學邊實作練習,十分方便! MORE
Python 安裝與基礎教學 本篇文章主要為資料科學導論中的 Python 程式語言的基礎教學,用於描述如何安裝 Python 環境以及 Python 相關基礎語法介紹。 MORE
Python Tutorial python 技術手冊,內容以python3.5為主,適合從基礎開始學習。 MORE
Python爬蟲新手筆記 提供給新手的參考筆記,流程說明與參考程式碼,練習範例取得成就感。 MORE
從零開始學資料科學:Numpy 基礎入門 本系列文章將透過系統介紹資料科學(Data Science)相關的知識,透過 Python 帶領讀者從零開始進入資料科學的世界。這邊我們將介紹 Numpy 這個強大的 Python 函式庫。 MORE
Pandas的基本介紹 包含基本function ( Series, DataFrame, Selection, Grouping ) 的介紹。 MORE

Hadoop Self-Learning

Date Title Description Post Links
Hadoop 官網 有Hadoop的Documentation、Related Projects、Wiki MORE
Hadoop Tutorial This is tutorialspoint web.There are Hadoop overview. MORE
2013-01-17 Hadoop 新 MapReduce 框架 Yarn詳解 新舊Hadoop框架的比較和介紹以及一些demo代碼 MORE
2014-10-01 Hadoop Distributed File System (HDFS) 介紹 HDFS架構圖、重要元件簡介:Name Node 、Secondary NameNode、 Backup Name Node、Data Node,和運作簡介 MORE
2016-01-05 10分鐘弄懂大數據框架Hadoop和Spark的差異 討論解決問題的層面、比較速率、災難恢復 MORE
2017-06-25 一步一步學習大數據:Hadoop 生態系統與場景 Hadoop的概要、相關組件介紹、集群硬件、設計目標和適用場景、架構解析;MapReduce工作原理和案例說明 MORE
2017-12-04 Hadoop Ecosystem 系列文簡介 Apache Hadoop、Apache HBase、Apache Hive、Apache Spark、Apache Solr等簡介和官方網站連結 MORE

Deep Learning Self-Learning

Date Title Description Post Links
Machine Learning Crash Course google開放的機器學習課程, 裡面除了有教學影片和課程練習題,也有一些programming exercises可作參考和練習 MORE
scikit-learn 有許多Python code Examples 可以參考 MORE
Machine Learning Course By Hsuan-Tien Lin 台大林軒田教授的開放式教學,內有PPT和Youtube教學影片的連接網址(Machine Learning Foundations and Machine Learning techniques) MORE
周莫煩的系列影片 很適合快速複習NN,影片很短,有大量生動動畫來解釋Gradient descent,CNN, RNN, LSTM以及其他等等,還有分析AlphaGo Zero的影片 MORE

Tensorflow Self-Learning

Date Title Description Post Links
TensorFlow 官網 install,develop, community,API,ecosystem MORE
TensorFlow 安裝 按照官網上安裝的教學步驟,使用python 的 virtual enviorment即可安裝成功 MORE
2018-03-08 TensorFlow 搭建神經網路教學 Tensorflow 基礎架構介紹及如何使用tensorflow 搭建神經網路 MORE
2018-04-27 TensorFlow 學習參考書 提供許多範例程式和範例程式下載 MORE
2018-05-08 Deep Learning 套件- Keras Keras是一款建立在Theano或Tensorflow上的高層神經網路API,適合想快速體驗機器學習的初學者 MORE
2018-05-08 Keras 儲存與讀取模型 儲存訓練好的模型'my_model.h5')(必須先安裝h5py模組)讀取:model=load_model('my_model.h5')預測:prediction=model.predict(x)(x的維度要跟訓練時一樣) MORE