Issue #15

September 8, 2019

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1. 1. Python Libraries for Interpretable Machine Learning [KDNuggets]
2. 2. Build a CRUD Web App With Python and Flask - Part One [scotch.io]
3. 3. Machine Learning Interpretability: Explaining Blackbox Models with LIME (Part II) [inovex.de]
4. 4. Data Scientists, The 5 Graph Algorithms that you should know [mlwhiz.com]
5. 5. Machine Learning Crash Course [developers.google.com]
6. 6. A Beginner's Guide To Understanding Convolutional Neural Networks [opendatascience.com]
7. 7. Clustering metrics better than the elbow-method [towardsdatascience.com]

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• • Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (T. L. Hayes, C. Kanan)
• • Cellular automata as convolutional neural networks (W. Gilpin)
• • Scientists who leave research to pursue other careers in science are still scientists (S. M. Hanlon)
• • Irrelevance of linear controllability to nonlinear dynamical networks (J. Jiang & Y.-C. Lai)
• • Field theory for recurrent mobility (M. Mazzoli, A. Molas, A. Bassolas, M. Lenormand, P. Colet, J. J. Ramasco)
• • Wikidata from a Research Perspective -- A Systematic Mapping Study of Wikidata (M. Farda-Sarbas, C. Mueller-Birn)
• • Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective (G.-H. Liu, E. A. Theodorou)
• • Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions (H. Hewamalage, C. Bergmeir, K. Bandara)
• • Fairness in Deep Learning: A Computational Perspective (M. Du, F. Yang, N. Zou, X. Hu)

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Tutorial: Deep Learning

All our videos are also available in our YouTube playlist.

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