Data Science & Machine Learning Newsletter #100
Posted on Fri 08 December 2017 in Data Science & Machine Learning Newsletter
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 The 10 Statistical Techniques Data Scientists Need to Master
 “data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”
 Unleash a faster Python on your data
 "Get real performance results and download the free Intel® Distribution for Python that includes everything you need for blazingfast computing, analytics, machine learning, and more. Use Intel Python with existing code, and you’re all set for a significant performance boost."
 What is a Bayesian Neural Network?
 "A Bayesian neural network (BNN) refers to extending standard networks with posterior inference."
 2018 Machine Learning Predictions from the Experts Themselves
 "As the data science community heads towards 2018, we asked our top speakers to comment on 2017’s most impactful achievements in Artificial Intelligence and make a few predictions for 2018. We summarize the most notable insights in this post, and offer expert commentary on the advancements, predictions and lessons learned regarding machine learning algorithms and deep learning systems."
 Understanding common misconceptions about pvalues
 "A pvalue is the probability of the observed, or more extreme, data, under the assumption that the nullhypothesis is true. The goal of this blog post is to understand what this means, and perhaps more importantly, what this doesn’t mean."

Analyzing tweets using Cloud Dataflow pipeline template
[caption id="attachment_2329" align="alignright" width="300"] The Dataflow pipeline graph[/caption]
 "This post describes how to use Google Cloud Dataflow templates to easily launch Dataflow pipelines from a Google App Engine (GAE) app, in order to support MapReduce jobs and many other data processing and analysis tasks."

A Unified Approach to Interpreting Model Predictions
 "Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations)"
 A blog series for text summarization