Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

Hexagonal binning with Hexbin: extract from the Visualization chapter

This reference is available…

The IEEE International Conference on Computer Vision received 4,303 papers and accepted 1,075 for the 2019 summit. Bellow is the best paper award.

Source: see paper listed below…

The title of the eBook is Dive in Deep Learning. Below I list the content of chapter 16, dealing with the math of deep learning. But the whole book (entirely free) is worth reading. This is an interactive deep learning book with code, math, and discussions. It is based on the NumPy interface.

Authors

• Aston Zhang, Amazon Senior Scientist, UIUC Ph.D.
• Zack C. Lipton, Amazon Scientist, CMU Assistant Professor, UCSD Ph.D.
• Mu Li,…

Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. 

Source: from the Support Vector Machines chapter,…

Another free book to learn Machine Learning. It also comes with a Youtube video series available here. 

Content

• Machine Learning Setup
• k-Nearest Neighbors / Curse of…

This list of lists contains books, notebooks, presentations, cheat sheets, and tutorials covering all aspects of data science, machine learning, deep learning, statistics, math, and more, with most documents featuring Python or R code and numerous illustrations or case studies. All this material is available for free, and consists of content mostly created in 2019 and 2018, by various top experts in their respective fields. A few of these documents are available on LinkedIn: see last section…

Interesting GIF visualization. We do not endorse any political opinion, and the picture below is provided only for its visual value, not for its political content.

It was originally posted here. Other visualizations can…

By Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. Dated June 24, 2019. This is not the same book as The Math of Machine Learning, also published by the same department at Berkeley, in 2018, and also authored by Garret…

According to Wikipedia, an ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.

In ANN implementations, the "signal" at a connection is a real number, and the output of each neuron is computed by some…

Technical paper, published in IEEE Xplore.

Abstract:

Written by Jun Wu.

Every year since I worked on wall street, traditional trading-is-an-art-form traders are leaving and retiring.

Their jobs are replaced by a new breed of experts who are savvy with numbers, systems, and the market. These experts don’t sleep, eat or drink. They are the AI Systems that can run on a thousand machines to…

The problem has to do with sampling, random numbers and probability distributions, so it is of interest to our community. As Scott Aaronson describes it in his blog, here is the problem:

You can read more here, including answers to…

This document is an attempt to provide a summary of the mathematical background needed for an introductory class in machine learning, which at UC Berkeley is known as CS 189/289A.

Our assumption is that the reader is already familiar with the basic concepts of multivariable calculus and linear algebra (at the level of UCB Math 53/54). We emphasize that this document is not a replacement for the prerequisite classes. Most subjects presented here are covered rather minimally; we intend…

The charts below represents the main findings of some recent analysis of 1,000 data scientist LinkedIn profiles, using a web scraper. It was limited to Singapore, and for people having "data scientist" on their profile. Of course, many have a different job title especially in fields such as Fintech (quant engineer) or Healthcare (biostatistician) but the findings are interesting nevertherless and seem to apply to other locales as well.

The first chart features the educational…

Interesting analysis done in R, about salaries of R developers broken down by country, featuring salary range and median salary. 

The dataset consists of survey answers from nearly 90,000 respondents. About 5,000 of them reported using R for “extensive development work over the past year”. The first filter used reduces the dataset from 88,883 respondents to 5,048. The second filter…

I found an interesting websites featuring hundreds of charts derived from US census data. It shows contrasts between states, cities, regarding education, jobs, languages spoken, salaries, even discrepencies between men and women or Asians and Caucasians, regarding various metrics broken down by location, education, or other criteria. I selected four of these charts.…

This chart communicates the same insights as a contour plot. What is interesting is the choice of hexagonal buckets (rather than squares) to aggregate data. In fact, any tessellation would work, in particular Voronoi tessellations.…

Not to be confused with this free Microsoft book with same title.

Data science underlies Amazon's product recommender, LinkedIn's People You Know feature, Pandora's personalized radio stations, Stripe's fraud detectors, and the incredible insights arising from the world's increasingly ubiquitous sensors. In the future,…

I stumbled upon this book by chance, when searching for material about time series (probably the most interesting chapter in this collection.) The various chapters are accessible from the top tabs, on this web page. It is mostly about R, but it has a few interesting chapters on statistical science too. Below is a…

By Avrim Blum, John Hopcroft, and Ravindran Kannan (2018). 

Computer science as an academic discipline began in the 1960s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. In the 1970s, the study of algorithms was added as an important component of…