Every Monday, we take a step back and look at all the cool stuff that went down during the previous week. Last week, we had news coming from all direction with some announcements *cough* Quarkus *cough* stealing the spotlight!
Worried your ML models might blab about proprietary data? Now, developers can keep their training data isolated from their machine learning models with TensorFlow Privacy. This Python library optimizes ML models without running into any data security or privacy concerns with differential privacy.
Machine learning for mobile and Internet of Things devices just got easier. With the latest updates to TensorFlow Lite 1.0, ML heads towards your smart phone and smart home. See what new things the TensorFlow Dev Summit 2019 brings to the table.
Learning a language is difficult enough for humans; imagine how hard it must be for your neural network. Thanks to Lingvo, TensorFlow has a new framework for sequencing models for language tasks like machine translation, speech recognition, and speech synthesis. Now, who knows the word for “framework” in Esperanto?
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.