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Top KDnuggets tweets, Jun 28-Jul 4: Cheat Sheet of #MachineLearning and #Python Cheat Sheets; Learning #DeepLearning with #Keras
Also: Train your #deeplearning model faster and sharper — two novel techniques; Lecture Collection - Natural Language Processing with #DeepLearning (Winter 2017) [Stanford]; #ICYMI 10 Free Must-Read Books for #MachineLearning and #DataScience
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Spotlight on the Remarkable Potential of AI in KYC (Know Your Customer)
Most people would have heard of the headline-making tremendous achievements in artificial intelligence (AI): Systems defeating world champions in board games like GO and winning quiz shows. These are small realizations of AI, but there is a silent revolution taking place in other areas, including Regulatory Compliance in Financial Services.
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How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 3
In this last post of the series, I describe how I used more powerful machine learning algorithms for the click prediction problem as well as the ensembling techniques that took me up to the 19th position on the leaderboard (top 2%)
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 Applying Deep Learning to Real-world Problems
In this blog post I shared three learnings that are important to us at Merantix when applying deep learning to real-world problems. I hope that these ideas are helpful for other people who plan to use deep learning in their business.
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Deep Learning with R + Keras
Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple’s CoreML, and Theano. It is becoming the de factor language for deep learning.
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Does Machine Learning Have a Future Role in Cyber Security?
In the past, ML learning hasn't had as much success in cyber security as in other fields. Many early attempts struggled with problems such as generating too many false positives, which resulted mixed attitudes towards it.
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The Surprising Complexity of Randomness
The reason we have pseudorandom numbers is because generating true random numbers using a computer is difficult. Computers, by design, are excellent at taking a set of instructions and carrying them out in the exact same way, every single time.
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 Top 15 Python Libraries for Data Science in 2017
Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.
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Deep Learning Papers Reading Roadmap
The roadmap is constructed in accordance with the following four guidelines: from outline to detail; from old to state-of-the-art; from generic to specific areas; focus on state-of-the-art.
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Deep Learning: TensorFlow Programming via XML and PMML
In this approach, problem dataset and its Neural network are specified in a PMML like XML file. Then it is used to populate the TensorFlow graph, which, in turn run to get the results.
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Applying Deep Learning to Real-world Problems
Top 15 Python Libraries for Data Science in 2017