#TensorFlow

TensorFlow 2.0 is on its way! What can we expect from this long-awaited upgrade to one of the most popular machine learning projects? A sneak peek at the preview version suggests a cleaner API, eager execution, and a tighter integration with tf.keras.

2019 is just around the corner so we’d like to find out which technologies will dominate next year, which technologies will fall behind and what’s going to stay static. We received over 350 votes so we humbly thank you for your participation.

Deep Learning is all the hype these days, beating another record most every week but writing code for deep learning is not just coding – it really helps if you have a basic understanding of what’s going on beneath. In this session from last year’s ML Conference, Sigrid Keydana offers a quick lesson on deep learning, as well as some tips and tricks for developers who’d like to dip their toes into this topic.

Space is big and getting bigger every second. So, astronomers need a tool that scales well. Introducing CosmoFlow, a TensorFlow-based tool designed to help find dark matter and predict cosmological parameters on supercomputers.

When the readily available tools won’t cut it, build a new one! And this is exactly what LinkedIn did to natively run TensorFlow on Apache Hadoop; TonY is now open source. Let’s have a look at what’s under this framework’s hood!

Deep learning is always among the hottest topics and TensorFlow is one of the most popular frameworks out there. In this session, Khanderao Kand goes through some deep learning concepts in general and TensorFlow and Apache Spark in specific.

TensorFlow 1.9 is here! So what does this latest update mean for the popular machine learning project? For starters, there’s an improved tf.keras beginner’s guide. For everyone else, there’s eager execution, improved GRU and LSTM implementation, and gradient boosted trees estimators.

Joining companies such as eBay and Google, Twitter now uses TensorFlow as its machine learning framework. TensorFlow continues to be a fan favorite in the framework wars and it’s no wonder why more and more companies are adopting the technology.

It’s time to take a look at the hot list for the first quarter of 2018. Blockchain and Tensorflow lead the way, but there are some surprises further down the list. Who’s in, who’s out, and what should freelancers focus their energies on?

Get your bags packed, it’s time to migrate your machine learning models from TensorFlow into Deeplearning4j. This trip is a lot easier than you’d think, but there are still some pitfalls for the unwary.

Google’s machine learning framework TensorFlow is on the rise. We spoke to Christoph Henkelmann at ML Conference 2017 about its benefits in the enterprise and the reasons for using Java in this context. Furthermore, we talked about new trends in the world of machine learning.

TensorFlow 1.7 has just arrived. We take a look at one of the cool new features in the latest release: full integration for TensorRT! What does that mean for our favorite machine learning project? Faster performances, for one thing.

If you want to stay up-to-date on the hottest projects that everyone is talking about, you should start by bookmarking GitHub Trending — a list of trending repositories based on the number of stars they receive. This month, Flutter is the second most starred repository; could it have something to do with its first beta release? *cough*rhetorical question*cough*

Every year, Stack Overflow asks the developer community about everything from their favorite technologies to their job preferences. As always, we focus on the most popular technologies but we also skim through the most dreaded languages, libraries, frameworks and tools.