# Neural Translation of Musical Style ## Introduction Neural networks have been in the spotlight recently. This isn’t a big surprise as they are producing incredible results in a variety of problem spaces. Recently, speech recognition reached human parity with the expressive power of neural networks [ 1 ]. The amazing thing is that they were actually introduced in 1943 but have only now become popular [ 2 ]. This is because of the availability of large amounts of data and GPU parallelisation. These result
Amir Masoud Sefidian
[Note: this material was authored by mentor Gordon Robinson and is the contents of a thread he created on the Coursera Forums of the previous version of the course. I’m bringing it over to the new Discourse platform with…
Hierarchical attention leverages multi-level data structures in neural networks to boost efficiency, expressivity, and interpretability across diverse domains
Abstract page for arXiv paper 1511.06939: Session-based Recommendations with Recurrent Neural Networks
To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for
The human mind can do tasks that an artificial neural network (ANN) cannot. Because the brain works like an ANN, the mind cannot just be what the brain does
Explore recent breakthroughs in neural networks for image recognition, highlighting key findings, innovative techniques, and emerging trends shaping the field
Bayesian Neural Networks place probability distributions over network weights instead of point estimates, enabling models to quantify how uncertain they are
A starling galley of phantasmagoric images generated by a neural network technique has been released. The images were made by some computer scientists associated with Google who had been using neural networks to classify objects in images. They discovered that by using the neural networks "in reverse" they could elicit visualisations of the representations that