Case Introduction This case will demonstrate how to use Recurrent Neural Networks (RNNs) for time series prediction. Specifically, we will use RNNs to predict the future values of a variable that depends on its own historical values. In this case, we will use a temperature dataset. We will provide the temperature values from the past … Read more
Author: Jaime Zornoza, Technical University of Madrid Translation: Chen Zhiyan Proofreading: Wang Weili This article is approximately 3700 words, and it is recommended to read in 10+ minutes. This article will help you understand deep learning neural networks in a way never seen before, and build a Chatbot using NLP! Have you ever fantasized about … Read more
Author: Debarko De Source: Hackernoon, Machine Heart What is a Recurrent Neural Network (RNN)? How do they work? Where can they be used? This article attempts to answer these questions and also showcases an RNN implementation demo that you can expand upon as needed. RNN Architecture Basic knowledge. Familiarity with Python and CNN is essential. … Read more
Click on the above “Mechanical and Electronic Engineering Technology” to follow us When processing images or other spatially structured data, Convolutional Neural Networks (CNN) are a commonly used deep learning model. The design inspiration of CNN comes from the visual processing method of the human brain. Unlike traditional fully connected neural networks, CNN extracts local … Read more
Click the "Beginner's Visual Learning" above, select "Star" or "Top" Heavy content delivered to you first Researchers proposed the concept of CNN (Convolutional Neural Networks) while studying image processing algorithms. Traditional fully connected networks are a black box – they take all inputs and pass each value through a dense network, then to a hot … Read more
Source: Xiao Bai Learns Vision This article is about 4500 words long and suggests reading for more than 10 minutes. It illustrates the entire architecture of neural networks and tools and techniques for understanding specific modules. Baseline Model AlexNet is a groundbreaking architecture that has made convolutional networks (CNN) the primary machine learning algorithm for … Read more
Nowadays, after becoming proficient in using dedicated frameworks and high-level libraries like Keras, TensorFlow, or PyTorch, we no longer need to frequently worry about the size of neural network models or remember formulas for activation functions and derivatives. With these libraries and frameworks, creating a neural network, even one with a complex architecture, often only … Read more
Click the "Xiaobai Learns Vision" above, select "Star" or "Pin" Heavy content delivered to you first Author丨Piotr Migdał Source丨Youer’s Cabin Editor丨Jishi Platform Jishi Guide Illustrated overview of the entire neural network architecture, and tools and techniques for understanding specific modules. Baseline Model AlexNet is a groundbreaking architecture that made Convolutional Neural Networks (CNNs) the main … Read more
2020 “High Download Papers in Chinese and English” Special Series In 2020, the “Journal of China Nonferrous Metals” published a total of 608 papers in both Chinese and English, some of which have shown significant influence. According to the China National Knowledge Infrastructure download data (as of March 3, 2021), the highest single paper download … Read more
Author: Pulkit Sharma Translation: Wang Weili Proofreading: Ding Nanya This article is about3400 words, recommended reading time is10 minutes. This article introduces the process of building a deep learning model for image recognition. By stating the problem from an actual competition, introducing the model framework, and showcasing the solution code, it provides beginners with a … Read more