Tech News

Why enterprises are turning from TensorFlow to PyTorch

A subcategory of machine learning, deep learning uses multi-layered neural networks to automate historically difficult machine tasks—such as image recognition, natural language processing (NLP), and machine translation—at scale.

TensorFlow, which emerged out of Google in 2015, has been the most popular open source deep learning framework for both research and business. But PyTorch, which emerged out of Facebook in 2016, has quickly caught up, thanks to community-driven improvements in ease of use and deployment for a widening range of use cases.

PyTorch is seeing particularly strong adoption in the automotive industry—where it can

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How to create ggplot labels in R

Hi. I’m Sharon Machlis at IDG Communications, here with Episode 54 of Do More With R: Add text labels to your ggplot graphs.

Labeling all or some of your data with text can be helpful in telling a story – even when your graph is using other cues like color and size. ggplot has built-in ways of doing this. And, the ggrepel package adds some functionality to those. Let’s take a look at how those work.

For this demo, I’ll start with a scatter plot looking at known Covid-19 cases per capita in Massachusetts counties. Here I’m interested in whether

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On-premises data warehouses are dead

Global Market Insights estimates that cloud providers will host the majority of data warehousing loads by 2025. But don’t take their word for it. Gartner estimates that 30 percent of data warehousing workloads now run in the cloud and that this will grow to two-thirds by 2024. Just a few years ago in 2016 the figure was less than 7 percent, also according to Gartner.   

None of this should be a surprise. Even the core data warehouse technology providers have seen this trend and are spending the majority of their R&D budgets to build solutions for public cloud providers.

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Apple releases TensorFlow fork with speedups for M1 Macs

Apple has released its own fork of the TensorFlow 2.4 machine learning framework, specifically optimized for its newly released M1 processor.

According to Apple, the M1-compiled version of TensorFlow delivers several times faster performance on a number of benchmarks, compared to the same jobs running on an Intel version of the same 2020 edition MacBook Pro.

The fork, available as open source, requires MacOS 11.0 or better, and provides accelerations on Macs running the new M1 processor.

Existing TensorFlow scripts run as-is with the fork; they do not need to be reworked to take advantage of its performance

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Today’s data science roles won’t exist in 10 years

In the coming decade, the data scientist role as we know it will look very different than it does today. But don’t worry, no one is predicting lost jobs, just changed jobs.

Data scientists will be fine — according to the Bureau of Labor Statistics, the role is still projected to grow at a higher than average clip through 2029. But advancements in technology will be the impetus for a huge shift in a data scientist’s responsibilities and in the way businesses approach analytics as a whole. And AutoML tools, which help automate the machine learning pipeline from raw

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Stargate: A new way to think about databases

As with many corporate-sponsored open source projects, Stargate becomes most interesting when it gets beyond its roots. DataStax open sourced Stargate “because we got tired of using different databases and different APIs depending on the work that we were trying to get done.” Billed as an “open source API framework for data,” the project aims to offer “a framework that can serve many APIs for a range of workloads.”

And yet Stargate starts with Apache Cassandra, the database upon which DataStax has built its business. For analyst Tony Baer, Stargate “could eventually turn Apache Cassandra into a multi-model database,”

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