DataScienceQuant

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A Gentle Introduction to Normality Tests in Python

An important decision point when working with a sample of data is whether to use parametric or nonparametric statistical methods.Parametric …

Data Science

Scalable and accurate deep learning with electronic health records

Article |<p>Open<p>Alvin Rajkomar ORCID: orcid.org/0000-0001-5750-5016,<br>• Eyal Oren,<br>• Kai Chen,<br>• Andrew M. Dai,<br>• Nissan Hajaj,<br>• Michaela Hardt,<br>• Peter J. Liu,<br>• Xiaobing …

Machine Learning

AI researchers allege that machine learning is alchemy

Ali Rahimi, a researcher in artificial intelligence (AI) at Google in San Francisco, California, took a swipe at his field last December—and received …

Machine Learning

27 Great Articles About Machine Learning Algorithms

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, …

Machine Learning

Your Data Science News Source for AI & Beyond

Rposted by John Mount Apr 13, 2018<p>Another R tip. Need to replace a name in some R code or make R code re-usable? Use wrapr::let(). Here is …

TensorFlow Hub | TensorFlow

Introduction<p>TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models. A <b>module</b> …

Your Data Science News Source for AI & Beyond

The strategy to turn the raw data into actionable insights is to integrate and analyze data from all data...<p>Modelingposted by Naveen Joshi Apr 12, …

FAQ:How are the likelihood ratio, Wald, and Lagrange multiplier (score) tests different and/or similar?

A researcher estimated the following model, which predicts high versus low writing scores on a standardized test (<b>hiwrite</b>), using students’ gender</b> …

Discriminant Analysis: Statistics All The Way

(This article was first published on <b> R-posts.com</b>, and kindly contributed to R-bloggers)Discriminant analysis is used when the variable to be …

Data Science

Notes on the Frank-Wolfe algorithm, Part I

$$ \def\xx{\boldsymbol x} \def\yy{\boldsymbol y} \def\ss{\boldsymbol s} \def\dd{\boldsymbol d} \DeclareMathOperator*{\argmin}{{arg\,min}} …

Algorithms

TensorFlow Tutorial For Beginners

<i>Originally published at</i> <i>https://www.datacamp.com/community/tutorials/tensorflow-tutorial</i><p>Deep learning is a subfield of machine learning that is a set …

Black-box optimization — Graduate Descent

Black-box optimization algorithms are a fantastic tool that everyone should be aware of. I frequently use black-box optimization algorithms for …

Essentials of Deep Learning – Sequence to Sequence modelling with Attention (using python)

Introduction<p>Deep Learning at scale is disrupting many industries by creating chatbots and bots never seen before. On the other hand, a person just …

Ten Machine Learning Algorithms You Should Know to Become a Data Scientist

Machine Learning Practitioners have different personalities. While some of them are “I am an expert in X and X can train on any type of data”, where …

13 Great Articles and Tutorials about Correlation

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, …

Data Science

Train and host Scikit-Learn models in Amazon SageMaker by building a Scikit Docker container | Amazon Web Services

It is especially important to know the correct training input location (/opt/ml/input/data/) and model location (/opt/ml/model/) for your program …

DevOps

How to Calculate the Principal Component Analysis from Scratch in Python

By onMarch 2, 2018 in Linear Algebra<p>An important machine learning method for dimensionality reduction is called Principal Component Analysis.<p>It is a …

Nice Generalization of the K-NN Clustering Algorithm -- Also Useful for Data Reduction

I describe here an interesting and intuitive clustering algorithm (that can be used for data reduction as well) offering several advantages, over …

Machine Learning

Data Visualization – Part 3

What Type of Data Visualization Do You Choose (if any)?<p>Determining whether or not you need a visualization is <b>step one</b>. While it seems silly, this is …

Data Science for Good, Part 1

Introduction<p>This is the first a three-article series about Data Science for Good. This article explains what what this idea is about and how you can …

Top 5 Data Science Machine Learning Repositories on GitHub in Jan 2018

Introduction<p>Breakthroughs in data science and machine learning are happening at a break-neck pace. If you are working in this field, it’s extremely …

Machine Learning Trick of the Day (7): Density Ratio Trick

A probability on its own is often an uninteresting thing. But when we can compare probabilities, that is when their full splendour is revealed. By …

Choosing the right activation function in a neural network

<b>Choosing the right activation function in a neural network</b><p>Activation functions are one of the many parameters you must choose to gain optimal success …

We Want to be Playing with a Moderate Number of Powerful Blocks

Many data scientists (and even statisticians) often suffer under one of the following misapprehensions:• They believe a technique doesn’t work in their …

Data Science

Berkeley AI Materials

Project 3: Reinforcement Learning<p>Version 1.001. Last Updated: 08/26/2014.<p>Table of Contents<p>Introduction<br>• Welcome<br>• Q1: Value Iteration<br>• Q2: Bridge Crossing …

Credit Modeling with Dask

This post explores a real-world use case calculating complex credit models in Python using Dask. It is an example of a complex parallel system that …

Python Programming

Making Sense of the Bias / Variance Trade-off in (Deep) Reinforcement Learning

What goes into a stable, accurate reinforcement signal?<p><i>(This post assumes some familiarity with machine learning, and reinforcement learning in</i> …

Machine Learning

Counting Efficiently with Bounter pt. 2: CountMinSketch

In my previous post on the new open source Python Bounter library we discussed how we can use its <i>HashTable</i> to quickly count approximate item …

Data Science

Data Science Live Book (open source) ~ new big release! 200-pages - Open Data Science - Your News Source for AI, Machine Learning & more

Well after some time, and +300 commits, this is the biggest release of the <b>Data Science Live Book!</b> (open source), after the first publication more …

Data Science

Step by Step Tutorial: Deep Learning with TensorFlow in R

(This article was first published on <b>R – nandeshwar.info</b>, and kindly contributed to R-bloggers)<p>Deep Learning with TensorFlow<p>Deep learning, also known …