Learn about long short-term memory networks. Comprehensive guide with examples, use cases, and best practices for LSTM architecture, gates, time series

I train neural networks, which are a type of computer program that try to learn to copy human things by looking at examples. By giving them just a list of names and no further instructions, I can get neural networks to invent names for paint colors

pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Abstract page for arXiv paper 1809.09349: The jamming transition as a paradigm to understand the loss landscape of deep neural networks

A convolutional neural network (CNN) is a deep learning architecture consisting of interconnected layers that process grid-structured data, such as images, by applying learnable filters through convol

I'm interested in getting people reactions to this paper: The world as a neural network. Here's a fragment of the abstract that omits the technical parts: We discuss a possibility that the entire universe on its most fundamental level is a neural network...This shows that the learning dynamics of a neural network can indeed exhibit

「メモリネットワーク」は代表的な記憶装置付きニューラルネットワークである． 本稿ではメモリモデル (記憶装置付きニューラルネットワーク) をいくつか概説し，論文 2 紙 (1) Memory Networks, (2) Towards AI-Complete Question Answering

这次回顾ECE408 Lecture 12，这次介绍了卷积神经网络。 课程主页： https://wiki.illinois.edu/wiki/display/ECE408 搬运视频： https://www.youtube.com/playlist?list=PL6RdenZrxrw-UKfRL5smPfFFpeqwN3Dsz

The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning. Here we show for the first time that artificial neural networks with such neuromodulated pla

A novel deep-learning framework shows how to interpret and decode raw neural recordings, avoiding the need for strong prior hypotheses, revealing a novel representation of head direction