Posts

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）...		 MORE

Videos

Date Title Youtube Links
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

Websites

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

Tutorials

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

Tools

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
 MORE

Papers

  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.