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

Weekly Selection – May 18, 2018

Unsupervised Learning with Python


by Vihar Kurama – 7 min read

Unsupervised Learning is a class of Machine Learning techniques to find the patterns in data. The data given to unsupervised algorithm are not labelled, which means only the input variables(X) are given with no corresponding output variables.


How to build your own Neural Network from scratch in Python

by James Loy – 6 min read

As part of my personal journey to gain a better understanding of Deep Learning, I’ve decided to build a Neural Network from scratch without a deep learning library like TensorFlow. I believe that understanding the inner workings of a Neural Network is important to any aspiring Data Scientist.


Google’s AutoML will change how businesses use Machine Learning

by George Seif – 6 min read

Google’s AutoML is a new up-and-coming (alpha stage) cloud software suite of Machine Learning tools. It’s based on Google’s state-of-the-art research in image recognition called Neural Architecture Search (NAS).


The Ultimate Cheat Sheet on Tableau Charts

by Kate Strachnyi – 11 min read

Tableau Desktop is an awesome data analysis and data visualization tool. It allows you to see your data immediately (with a few drag and drops). The "Show Me" feature is extremely helpful especially for those just starting out with Tableau.


FIFA World Cup 2018: A Data-Driven Approach to Ideal Team Line-Ups

by James Le – 8 min read

With the World Cup 2018 coming up this summer in Russia, every soccer fan around the world is eager to make his prediction on what team will win this year. Another looming question for the fans is how their favorite national teams should line up: What formation should be used? Which players should be chosen? Which ones should be left on the bench or eliminated from the tournament?


3 Small But Powerful Convolutional Networks

by Arthur Douillard – 5 min read

Many CNN architectures have been developed to attain the best accuracy on ImageNet. Computing power is not limited for this competition, why bother?


Detecting Pikachu in videos using Tensorflow Object Detection

by Juan De Dios Santos – 7 min read

Deep inside the many functionalities and tools of TensorFlow, lies a component named TensorFlow Object Detection API. The purpose of this library, as the name says, is to train a neural network capable of recognizing objects in a frame, for example, an image.


Predicting the Survival of Titanic Passengers

by Niklas Donges – 20 min read

In this blog-post, I will go through the whole process of creating a machine learning model on the famous Titanic dataset, which is used by many people all over the world. It provides information on the fate of passengers on the Titanic, summarized according to economic status (class), sex, age and survival.


Hyperparameter Optimization with Keras

by Mikko – 15 min read

With the right process in place, it will not be difficult to find state-of-the-art hyperparameter configuration for a given prediction task. Out of the three approaches – manual, machine-assisted, and algorithmic – this article will focus on machine-assisted.


Related Articles