Follow Hui Kuo Technology to learn more about technology knowledge Hello everyone, I am Liu Zenghui! Today we will continue the series of lectures on artificial intelligence neural networks, focusing on the transformation mechanism and practical applications of RNNs, exploring how they are widely used in various fields! Transformation Mechanism of RNN In the previous … Read more
0Introduction Nowadays, discussions about machine learning, deep learning, and artificial neural networks are becoming more and more prevalent. However, programmers often just want to use these magical frameworks without wanting to know how they actually work behind the scenes. But if we could grasp these underlying principles, wouldn’t it be better for us to use … Read more
More AI Related Readings: What Can Machine Learning Do? Four Leading Companies Answer This Estimating Brain Age Using MRI Data Introduction to GPipe: An Open Source Library for Efficient Training of Large Neural Network Models
You may be familiar with reinforcement learning, and you may also know about RNNs. What sparks can these two relatively complex concepts in the world of machine learning create together? Let me share a few thoughts. Before discussing RNNs, let’s first talk about reinforcement learning. Reinforcement learning is gaining increasing attention; its importance can be … Read more
Starting the Journey of RNN, Commonly Known Four Structures of RNN One to One: This is the traditional application of neural networks, usually used for simple input to output tasks. For example, in image classification, the network receives an image as input and identifies the category of the object represented in the image. Specifically, suppose … Read more
This article introduces the ICML 2020 paper “Do RNN and LSTM have Long Memory?“. The authors of the paper are from Huawei Noah’s Ark Lab and the University of Hong Kong.. Author | Noah’s Ark Lab Editor | Cong Mo Paper link: https://arxiv.org/abs/2006.03860 1 Introduction To overcome the difficulties of Recurrent Neural Networks (RNNs) in … Read more
Machine Heart Column This column is produced by Machine Heart SOTA! Model Resource Station, updated every Sunday on the Machine Heart public account. This column will review common tasks in natural language processing, computer vision, and other fields, and detail the classic models that have achieved SOTA on these tasks. Visit SOTA! Model Resource Station … Read more
Machine Heart Compilation Author: Sander Dieleman Editor: Panda W Diffusion models are very popular, and their descriptions vary widely. In this article, a DeepMind research scientist provides a comprehensive analysis of the topic “What is a diffusion model?” If you’ve tried one of the most popular AI painting tools, Stable Diffusion, then you’ve already experienced … Read more
Source | Zhihu Author | JerryFly Link | https://zhuanlan.zhihu.com/p/115823190 Editor | Deep Learning Matters WeChat Official Account This article is for academic exchange only. If there is any infringement, please contact us for deletion. RNN is commonly used to handle sequential problems. This article demonstrates the computation process of RNN using animated graphics. The three … Read more
Selected from arXiv Authors: Matthew MacKay et al. Translated by: Machine Heart Contributors: Gao Xuan, Zhang Qian Recurrent Neural Networks (RNNs) achieve the best current performance in processing sequential data, but they require a large amount of memory during training. Reversible Recurrent Neural Networks provide a way to reduce the memory requirements for training, as … Read more