# Recurrent Published articles for Recurrent. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Submissions open for Godot 2026 showreel DevFeed: [Submissions open for Godot 2026 showreel]() Original publisher: [Read original article]() Author: Emi Published: 2026-08-15T13:00:00Z Content type: article Language: en Sources: [Godot Engine Official]() Topics: [Godot](), [Development](), [App]() Tags: [2026](), [development](), [games](), [maintainers](), [news](), [people](), [projects](), [recurrent](), [tools](), [videos]() ### AI overview Godot invites creators to submit videos of published or in-progress games and tools for the 2026 Godot Showreel. Submissions close on October 1, 2026, followed by voting from October 2 through October 16. ### Source excerpt Showcase your Godot creations in 2026! We're inviting submissions of short videos featuring your published or in-progress Godot games or tools. Don't miss this chance to be a part of it! ## Titans + MIRAS: Helping AI have long-term memory DevFeed: [Titans + MIRAS: Helping AI have long-term memory]() Original publisher: [Read original article]() Published: 2025-12-04T19:26:09Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [Transformer architecture](), [Google](), [Mamba](), [Neural Network](), [Compression]() Tags: [ai](), [architecture](), [compression](), [context](), [generative-ai](), [machine-intelligence](), [mamba](), [natural-language-processing](), [neural-networks](), [offline](), [real-time](), [recurrent]() ### AI overview Google Research introduces Titans and the MIRAS framework, which combine recurrent-model speed with transformer-like accuracy for handling very long contexts. The approach uses test-time memorization and an adaptive neural long-term memory that updates as data streams in, without offline retraining. ### Source excerpt Generative AI ## Recurrent Networks Hello World in Clojure with new Deep Diamond RNN support on CPU and GPU DevFeed: [Recurrent Networks Hello World in Clojure with new Deep Diamond RNN support on CPU and GPU]() Original publisher: [Read original article]() Published: 2022-08-26T14:25:00Z Content type: tutorial Language: en Sources: [Dragan Djuric]() Topics: [Clojure](), [Deep learning](), [Time Series](), [Programming]() Tags: [clojure](), [cpu](), [deep](), [deep-learning](), [diamond](), [gpu](), [learning](), [networks](), [neural](), [programming](), [recurrent](), [rnn](), [time-series]() ### AI overview A beginner-friendly Clojure tutorial demonstrates recurrent neural networks in Deep Diamond by training a model to predict the next value in a simple numerical sequence. It introduces time-series prediction and notes that the example can run on CPU and GPU. ### Source excerpt I've been busy in the last period working on new major features in Deep Diamond, one of which is the support for Recurrent Neural Networks (RNN). It's not been an easy ride, but I can finally show you some results! Big thanks for everyone that's helping me with this by buying my books (or subscribing to the upcoming editions), and Clojurists Together, who generously funded me in the past year to work on this. I know that most of you probably don't have much more than passing familiarity with deep learning, let alone recurrent neural networks, and that's why I'll try to show a very simple example on the level of Hello World that anyone interested in machine learning and programming can understand and try. So, enough talk, let's get down to business. What are we doing We are demonstrating a hammer: recurrent neural networks. Just kidding; we would like to create a (software) device that can learn to predict the next data point in a series. Depending on the data, this can be done in a number of ways (even by convolutional neural networks (CNN) that Deep Diamond already supported), one of which is RNN. So, we are creating a recurrent network, and training it with a set of data for this task. An example of data that fits this task would be temperature at some place, stock prices, and any other (possibly infinite) sequence of numbers in one of more dimensions that have an ordinal relation, that is, has an abstract notion of time attached to it. Then, we are trying to forecast one or more values that are happening in the future (temperature the next day, or the closing price of a stock, etc.). Since Hello World has to be dead simple, a real example would have too many opaque numbers, so we'll solve an artificially trivial task of teaching our network to predict the next number in the series for obvious series such as 1, 2, 3, 4, 5. Of course, in real life we rarely need to solve that exact task with such a bazooka as RNN, but if this is your first contact with time series ## Deep learning experiments in OCaml DevFeed: [Deep learning experiments in OCaml]() Original publisher: [Read original article]() Author: Laurent Mazare Published: 2018-09-20T00:00:00Z Content type: tutorial Language: en Sources: [Jane Street]() Topics: [OCaml](), [Machine learning](), [Tensorflow](), [Programming](), [GPU]() Tags: [deep-learning](), [gpu](), [machine-learning](), [neural-networks](), [ocaml](), [programming](), [programming-language](), [recurrent](), [rnn](), [tensorflow]() ### AI overview This article describes reproducing classical deep learning experiments in OCaml instead of Python. It explains using TensorFlow bindings to train functional OCaml models on a GPU, including Neural Style Transfer and character-level language modeling, and discusses type-safe representations of TensorFlow computation graphs. ### Source excerpt Last year we held a machine learning seminar in our London office, which was an opportunity to reproduce some classical deep learning results with a nice twist: we used OCaml as a programming language rather than Python. This allowed us to train models defined in a functional way in OCaml on a GPU using TensorFlow.