Brought to you by explained.ai Explaining RNNs without neural networks Terence Parr Terence is a tech lead at Google and ex-Professor of computer/data science in University of San Francisco's MS in Data Science program and you might know him as the creator of the ANTLR parser generator. Vanilla recurrent neural networks (RNNs) form the basis of more sophisticated models, such as LSTMs and GRUs. There are lots of great articles, books, and videos that describe the functionality, mathematics, and behavior of

Learn how CNNs use learned spatial filters (kernels) and weight sharing to excel at image recognition and other grid-like data.

A low precision deep neural network training technique for producing sparse, ternary neural networks is presented. The technique incorporates hard- ware implementation costs during training to achieve significant model compression for inference. Training involves three stages: network training using L2 regularization and a quantization threshold regularizer, quantization pruning, and finally retraining. Resulting networks achieve improved accuracy, reduced memory footprint and reduced computational complexi

jarxiv Japanese arxiv コンテンツへスキップ ホーム ← Communities in the Kuramoto Model: Dynamics and Detection via Path Signatures Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data → Towards Efficient Training of Graph Neural Networks: A Multiscale Approach 投稿日: 2025年3月26日 作成者: jarxiv 要約 グラフニューラルネットワーク（GNNS）は

Spiking Neural Networks As Universal Function Approximators

Siamese Networks are a class of neural networks that are specialized for tasks involving comparison or verification between two comparable items. They are particularly useful in applications such as face recognition, signature verification, and anomaly detection, where the objective is to learn a similarity function that measures how similar or different two inputs are

Abstract page for arXiv paper 2110.08058: Quantifying Local Specialization in Deep Neural Networks

Explore the main similarities and differences between support vector machines and neural networks

In this blog post, we'll be discussing how to create a feedforward neural network in TensorFlow. We'll go over the theory behind feedforward neural networks

CNNs – the specialized AI models behind advanced visual inspection systems – automatically extract features from images and learn to identify defects with remarkable accuracy. The impact of CNNs extends across industries. In the medical field, for example, they surpass human pathologists by 19% in cancer detection accuracy – a critical difference that can reduce […]