Issue #19
October 6, 2019
- 1. We can't trust AI systems built on deep learning alone [technologyreview.com]
- 2. ML From Scratch, Part 6: Principal Component Analysis [oranlooney.com]
- 3. A Gentle Introduction to Bayes Theorem for Machine Learning [machinelearningmastery.com]
- 4. Statistics for Engineers - Applying statistical techniques to operations data [queue.acm.org]
- 5. Processing 40 TB of code from ~10 million projects with a dedicated server and Go for $100 [boyter.org]
- 6. Statistics for Data Scientists in 5 mins [towardsdatascience.com]
- 7. Data visualization with Plotly [medium.com/dataseries]
- • One neuron versus deep learning in aftershock prediction (A. Mignan, M. Broccardo)
- • Deep learning in neural networks: An overview (J. Schmidhuber)
- • Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method (Y. Xie, H. Jiang)
- • Tutorial on Implied Posterior Probability for SVMs (G. Nalbantov, S. Ivanov)
- • The vulnerability of communities in complex network: An entropy approach (T. Wen, Y. Deng)
- • An Introduction to Probabilistic Spiking Neural Networks (H. Jang, O. Simeone, B. Gardner, A. Grüning)
How Bayes Theorem works
All our videos are also available in our YouTube playlist.
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