TensorFlow Dev Summit 2020 took place last week and gave an overview of everything that’s been going on in the world of the machine learning library. While we have been covering TensorFlow, TensorFlow.js, and the recently open sourced TensorFlow Quantum, you may not yet have heard of TF Hub, TFX or TF Lite, so let’s see what they are all about.
Every Monday, we take a step back and look at all the cool stuff that went down during the previous week. Last week, JavaFX 14 was released, so we sat down with Johan Vos, Java Champion and Gluon co-founder, to discuss all that’s new. We also took a closer look at the advantages of using Clojure and welcomed the latest Apache NetBeans release.
The TensorFlow developers have been keeping busy this week: Not only has the first release candidate for TensorFlow 2.2 arrived, but we can now also welcome the very first release of TensorFlow Quantum. Let’s see what has been happening in the world of Google’s machine learning framework.
TensorFlow 2.1.0 has been released, following two release candidates. The final version of the machine learning platform includes new features and breaking changes. Meanwhile, Python 2.7 has reached its end of life and is no longer supported by TensorFlow. Let’s take a look at what else has changed.
The machine learning platform TensorFlow, currently in version 2.0, is making its way toward the minor release 2.1.0: TensorFlow 2.1.0-rc0 is the first release candidate and includes some breaking changes. The upcoming version will be the last to support Python 2.7.
Google introduced TensorFlow Enterprise, a new collection of machine learning services and products. The beta version includes managed services, and some versions will receive long-term version support for up to three years. At the same time, Google unveiled the new website TensorBoard.dev.
At Machine Learning Conference 2019 in Munich, Christoph Henkelmann gave a talk about TensorFlow training on the JVM. We recorded the whole thing, and now you can watch it here (including slides) to learn all about how to combine a TensorFlow model with Java.
Every Monday, we take a step back and look at all the cool stuff that went down during the previous week. Last week we welcomed the new version of Keras, got one step closer to TensorFlow 2.0 with a new release candidate, and learned more about the potentials of blockchain.
We are one step closer to TensorFlow 2.0.0. The new release candidate, 2.0.0-rc2 includes new features, improvements, breaking changes, and bug fixes. Catch up on what’s expected to arrive in TensorFlow 2.0. The next major release focuses on ease of use and simplicity, with plenty of updates and easy model building with Keras.
Keras version 2.3.0 is here, and it is the last major multi-backend release. Going forward, users are recommended to switch their code over to tf.keras in TensorFlow 2.0. This release brings API changes and a few breaking changes. Have a look under the hood and see what it includes, as well as what the plans are going forward.
Machine learning has many use cases and offers up a world of possibilities. However, some people might be put off and think it’s too difficult. It’s not. You don’t have to be a machine learning pro to use TensorFlow Lite. Here’s how to get started building your own customized machine learning model on Android.
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!