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Review: MXNet deep learning shines with Gluon
With the addition of the high-level Gluon API, Apache MXNet rivals TensorFlow and PyTorch for developing deep learning models
How to use Knime for data science
Free, open-source Knime allows you to visually assemble data processing “nodes” into machine learning, deep learning, and other analytics workflows
Machine learning megaguide: Amazon, Microsoft, Databricks, Google, HPE, IBM
Download InfoWorld's massive roundup of Amazon, Microsoft, Databricks, Google, HPE, and IBM machine learning toolkits
Microsoft revamps machine learning tools for Apache Spark
The new open source release integrates Spark with Cognitive Toolkit and other Microsoft machine learning offerings
![Do More With R [video teaser/video series] - R Programming Guide - Tips & Tricks](https://images.idgesg.net/images/article/2018/05/do_more_with_r_teaser-100758232-small.3x2.jpg)
Reshape data in R with the tidyr package
See how the tidyr R package’s gather and spread functions work. Plus a bonus look at labeling in ggplot2 with the directlabels package
The best open source software for machine learning
InfoWorld’s 2018 Best of Open Source Software Award winners in machine learning and deep learning
Why data science teams should operate like startups
Successful startups can teach data science teams a lesson or two.
Quick guide: Learn to crunch big data with R
Get started using the open source R programming language to do statistical computing and graphics on large data sets
Microsoft adds Python support to Power BI
A preview feature allows Python scripts to be used as data sources and as a way to create visualizations in Power BI Desktop
What’s new in TensorFlow machine learning
Google’s TensorFlow 2.0 beta is expected later this year, with a focus on improving performance and correcting mistakes in compatibility and continuity
Why there are no shortcuts to machine learning
As long as companies understand that good data science takes time in an enterprise, and give these people room to learn and grow, they won’t need shortcuts
What’s new in Julia: Version 1.0 is here
New package manager, better optimization debut in the first production release
Julia tutorial: Get started with the Julia language
Want the convenience of a dynamic language and the performance of a compiled statically typed language? Try Julia
Data is inherently messy. Is that really such a bad thing?
In an imperfect world, consider shifting your data quality mindset from “how do I clean all this up?” to “how do I make the most of this state of affairs?”
Practical use cases for real-time decisioning
Real-time decisioning has its place in almost every transaction and interaction you have with data and technology today
What’s new in the Anaconda distribution for Python
Anaconda 5.2 adds job scheduling, support for GPUs, and integration with version control systems including Git and GitHub
What’s new in Python 3.7
Python 3.7 adds new classes for data handling, optimizations for script compilation and garbage collection, and faster asynchronous I/O
What is Julia? A fresh approach to numerical computing
A “no compromises” programming language for data scientists, Julia combines the ease of a dynamic language with the speed of a compiled language
In an age of fake news, is there really such a thing as fake data?
The pitfalls and benefits of using synthetic data to train AI algorithms
Why dynamically visualizing relationships in data matters
The ability to visualize data, their relationships to one another and connections to business objectives gives organizations the power to uncover insights that would otherwise elude them
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