AI/ML Resources

Feature Hashing for Scalable Machine Learning

Feature hashing is a valuable tool in the data scientist's arsenal. Learn how to use it as a fast, efficient, flexible technique for feature extraction that can scale to sparse, high-dimensional data.

November 8, 2017

by Nick Pentreath

· 19,080 Views · 3 Likes

Optimizing K-Means Clustering for Time Series Data

Given the amount of data we collect, faster clustering times are crucial. Here's how we sped up our k-means clustering process!

October 30, 2017

by Jason Snell

· 26,096 Views · 2 Likes

Java: The Strategy Pattern

This deep dive into the classic Strategy pattern covers how it works, example usage, and deciding how and when to use it in your projects.

October 29, 2017

by Justin Albano

CORE

· 81,866 Views · 66 Likes

Dockerizing, Clustering, and Queueing Up With Mule Enterprise

In this tutorial, you'll learn how to build Mule instances and cluster instances, and Dockerize a Mule application to run in a container.

October 28, 2017

by Anirban Sen Chowdhary

· 10,533 Views · 5 Likes

Machine Learning Algorithms: Which One to Choose for Your Problem

Many articles about machine learning algorithms provide great definitions — but they don't make it easier to choose which algorithm you should use. Enter: this article!

October 27, 2017

by Daniil Korbut

· 26,565 Views · 34 Likes

How Neo4j Is Making Graph Technology More Accessible

In today's news: Neo4j is announcing Cypher for Apache Spark and the Neo4j Native Graph Platform. Come learn about it all, according to Neo4j's Head of Product Marketing.

October 24, 2017

by Tom Smith

CORE

· 5,629 Views · 2 Likes

Learning Neural Networks Using Java Libraries

Learn about the evolution of neural networks and get a summary of popular Java neural network libraries in this short guide to implementing neural networks from scratch.

October 22, 2017

by Daniela Kolarova

CORE

· 23,044 Views · 20 Likes

Reinforcement Learning for the Enterprise

Reinforcement learning is a first step towards artificial intelligence that can survive in a variety of environments instead of being tied to certain rules or models.

October 21, 2017

by Sibanjan Das

· 15,457 Views · 8 Likes

Changing Attitudes and Approaches Towards Privacy, AI, and IoT

Big data, IoT, and AI have all contributed to the widespread use of personal info. The privacy debate is at a crossroads where the public, authorities, and companies must decide in which direction the industry will turn.

October 20, 2017

by Ira Pasternak

· 9,476 Views · 6 Likes

AI-Powered NLP: The Evolution of Machine Intelligence From Machine Learning

With the advent of deep learning techniques, MI objectives like automated real-time question-answering, emotional connotation, fighting spam, and more are achieved.

October 19, 2017

by Tuhin Chattopadhyay

CORE

· 13,464 Views · 6 Likes

An Introduction to Caching: How and Why We Do It

When it comes to caching, what was once a nice-to-have it now a must-have. Check out this detailed article to learn everything you need to know about caching!

October 19, 2017

by Arun Tawara

· 172,467 Views · 47 Likes

TensorFlow for Real-World Applications

TensorFlow and deep learning are things that corporations must now embrace. The coming flood of audio, video, and image data and their applications are key to success.

October 17, 2017

by Tim Spann

CORE

· 32,568 Views · 10 Likes

Using Machine Learning to Retrieve Relevant CVs Based on Job Description

If you've ever tried to hire anyone, you know how difficult it can be to pour through hundreds of resumes and find the right one. AI can take the pain out of the process!

October 16, 2017

by Francis C. Fernandez-Reyes

· 51,101 Views · 14 Likes

Stream Processing With Apache Flink

See how to get started with writing stream processing algorithms using Apache Flink. by reading a stream of Wikipedia edits and getting some meaningful data out of it.

October 16, 2017

by Ivan Mushketyk

· 20,426 Views · 9 Likes

Best Practices: Performance Tuning Real Life MuleSoft APIs

In this post, we offer some insights into how to avoid pitfalls and achieve development efficiency, performance, and scalability.

October 16, 2017

by Ali Salman Rizvi

· 17,322 Views · 5 Likes

Video Analysis to Detect Suspicious Activity Based on Deep Learning

Learn how to build on AI system that can classify a video into three classes: criminal or violent activity, potentially suspicious, or safe.

October 14, 2017

by Cristyan Rufino Gil Morales

· 47,625 Views · 27 Likes

Getting Started With Batch Processing Using Apache Flink

If you've been following software development news recently you probably heard about the new project called Apache Flink. I've already written about it a bit...

October 13, 2017

by Ivan Mushketyk

· 16,160 Views · 9 Likes

Machine Learning: Past and Present

We've seen an explosion of interest in machine learning in the past few years. But where did machine learning come from and why is there so much interest in it now?

October 13, 2017

by John Manslow

· 337 Views · 4 Likes

Deep Learning vs. Machine Learning

If you have often wondered to yourself about the difference between machine learning and deep learning, read on to get a detailed comparison in simple layman language.

October 12, 2017

by Faizan Shaikh

· 19,340 Views · 6 Likes

A Trial Run With H2O AutoML in R: Automated Machine Learning Functionality

No more coding for different models, noting down the results, and selecting the best model — AutoML is going to do all of these for you while you brew a cuppa!

October 10, 2017

by Sibanjan Das

· 20,079 Views · 4 Likes

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