The world’s leading publication for data science, AI, and ML professionals.

Weekly Selection – Jan 12, 2018

Building a co-occurrence matrix with d3 to analyze overlapping topics in dissertations


by Déborah Mesquita – 7 min read

The goal of my master’s degree research is to spark new collaboration opportunities between researchers from different fields. But before doing that I need to take a step back and see if there is any collaboration happening already.


Spiking Neural Networks, the Next Generation of Machine Learning

by Devin Soni – 4 min read

Everyone who has been remotely tuned in to recent progress in machine learning has heard of the current 2nd generation artificial neural networks used for machine learning. These are generally fully connected, take in continuous values, and output continuous values.


The 8 Neural Network Architectures Machine Learning Researchers Need to Learn

by James Le – 22 min read

Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly.


Neural Network Optimization Algorithms

by Vadim Smolyakov – 6 min read

The neural network is represented by f(x(i); theta) where x(i) are the training data and y(i) are the training labels, the gradient of the loss L is computed with respect to model parameters theta. The learning rate (_epsk) determines the size of the step that the algorithm takes along the gradient (in the negative direction in the case of minimization and in the positive direction in the case of maximization).


What Data Science Reveals About President Trump and the GOP

by Parker Sewell – 8 min read

As a final project for a User-Generated Content Analytics class, our group of six wanted to know what Twitter users and commenters on articles in The New York Times thought of President Trump and the GOP. We hypothesized that most people would strongly associate Mr. Trump with the party he leads and vice-versa.


Tell Me a Story: Thoughts on Model Interpretability

by Cody Marie Wild – 6 min read

Recently, my thinking has circulated around what feel like some of Machine Learning’s biggest meta-conversations: the potential and limitations of learning a generally intelligent actor, the nuance and genuine normative challenge of algorithmic fairness, and, now, what it means for models to be interpretable and understandable for humans.


How to get a job as a Data Scientist?

by Favio Vázquez – 4 min read

Hi everyone. This blog post comes from 3 post I did recently at LinkedIn. Here they are Part 1, Part 2, and Part 3. This is a hard question to answer. Hang with me in this one (and this is not the final answer about the universe, existence and everything).


Sentiment Analysis: Concept, Analysis and Applications

by Shashank Gupta – 7 min read

Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count based metrics.


CryptoCurrency Price Prediction with Python

by Chalita Lertlumprasert – 11 min read

Ever since Bitcoin’s price began to skyrocket, there has been constant hype surrounding the crytocurrency market. Alternate coins keep popping up everyday- some are scams, some make it to the top coin list in months.


Predicting Wealth in New York City from FourSquare Check-ins

by Vincent Chen – 8 min read

In marketing and advertising, an understanding of local demographics allows enterprises to better cater their products and services towards the individuals who live there. In the academic world, social scientists might be interested in understanding how people in cities react to ever-changing businesses, perhaps towards the study of gentrification.


Understanding Feature Engineering (Part 2) – Categorical Data

by Dipanjan Sarkar – 14 min read

We covered various feature engineering strategies for dealing with structured continuous numeric data in the previous article in this series. In this article, we will look at another type of structured data, which is discrete in nature and is popularly termed as categorical data.


My Journey Into Data Science

by Rosebud Anwuri. – 8 min read

Quite a number of people have asked me about my switch from Chemical Engineering to Data Science. How did I do it? When did I do it? Why did I do it? I felt today (January 6, 2018) was a befitting day to answer these questions as it marks the third year since I enrolled for my first programming course.


Probability concepts explained: Bayesian inference for parameter estimation.

by Jonny Brooks-Bartlett – 14 min read

In the previous blog post I covered the maximum likelihood method for parameter estimation in machine learning and statistical models. In this post we’ll go over another method for parameter estimation using Bayesian inference.


We thanks our 43 Patreon supporters for making our work possible 😊

William Hogg, Chris Seidel, Ramesh Shalam, Daniel Shapiro, PhD, Venkat Raman, Kirill Panarin, Sai Sai, Zach Thigpen, Tirthajyoti Sarkar, Anthony Manello, Naveen Tirupattur, Luis Loret de Mola, Segah Meer, Jean Pierre and Dipanjan Sarkar.

Become a Patron


Related Articles