An Introduction to Reinforcement Learning Concepts
Individuals interested in reinforcement learning crowded into a room at ODSC Europe 2018. There, Badoo’s lead data scientist Leonardo De Marchi hosted a four-hour workshop to guide attendees through the first steps. What is reinforcement learning? Reinforcement learning is one machine learning approach. Most people know of supervised and unsupervised learning.... Read more
Building Neural Networks with Perceptron, One Year Later Part II
This is the second part in a three-part series. The first part can be read here. The new look If you have built neural networks with Perceptron before, you may be surprised to see it has a complete redesign (not available on Mac yet):   Application To get Perceptron... Read more
Building Neural Networks with Perceptron, One Year Later — Part I
Introduction Around one year ago now, I started writing for Open Data Science after presenting Perceptron at the ODSC conference. Since then, a lot has changed. People have found fascinating uses for the software, and also help contribute to it. In this series I’ll present a fresh overview of... Read more
Build a Multi-Class Support Vector Machine in R
Support Vector Machines (SVMs) are quite popular in the data science community. Data scientists often use SVMs for classification tasks, and they tend to perform well in a variety of problem domains. An SVM performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different... Read more
Efficient, Simplistic Training Pipelines for GANs in the Cloud with Paperspace
Generative adversarial networks — GANs for short — are making waves in the world of machine learning. Yann LeCun, a legend in the deep learning community, said in a Quora post “ the most interesting idea in the last 10 years in .” GANs (and, more generally,... Read more
Crash Course: Pool-Based Sampling in Active Learning
Active learning is a class of machine learning problems where labeled data isn’t available for supervised algorithms. Let’s take the classic setup as an example. Say we have pictures of birds and want to classify them by type, but the images don’t have labels for what kind of bird... Read more
Classic Regularization Techniques in Neural Networks
Neural networks are notoriously tricky to optimize. There isn’t a way to compute a global optimum for weight parameters, so we’re left fishing around in the dark for acceptable solutions while trying to ensure we don’t overfit the data. This is a quick overview of the most popular approaches... Read more
Insights Through Geospatial Data Visualization
Data visualization is an integral part of the data science process. “Data viz” plays an important role early in the process with exploratory data analysis (EDA) and also at the end with data storytelling for representing results for enterprise decision makers. In this article, we’ll review how geospatial plots... Read more
The Five Best Frameworks for Data Scientists
There are many tools that can help you when you start your data science career. Some of these tools you will be using them almost in every new project. In this post, we aim to highlight the five best frameworks for data scientists so that you can better immerse... Read more
Sentiment Analysis in R Made Simple
Sentiment analysis is located at the heart of natural language processing, text mining/analytics, and computational linguistics. It refers to any measurement technique by which subjective information is extracted from textual documents. In other words, it extracts the polarity of the expressed sentiment in a range spanning from positive to... Read more