Click on the above “Beginner Learning Vision”, select to add Star or “Top” Important content delivered immediately Introduction Artificial neural networks are typically optimized through a learning method based on mathematical statistics. This article provides a detailed introduction to the definition of neural networks and the relevant operational models. Overview of Structure 1. Introduction to … 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
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
Source: Algorithm Advancement This article is approximately 4800 words long and is suggested to be read in 8 minutes. This article introduces the functionality of neural networks. In the fields of machine learning and related areas, artificial neural networks are computational models inspired by biological neural networks: each neuron is connected to other neurons, and … Read more
1 Algorithm Introduction Convolutional Neural Networks (CNNs) are a class of feedforward neural networks that include convolutional computations and have a deep structure. They have emerged in recent years as an efficient recognition method that has gained widespread attention. The design inspiration for CNNs comes from the hierarchical processing capabilities of the animal visual system, … 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
Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered first! Written by | Lao Tao (Researcher from a certain company, hereditary parameter tuning) Translated by | Beautiful person with meticulous thoughts Introduction As the hottest topic in NLP over the past two years, the language pre-training technologies represented by ELMo/BERT are already familiar … Read more
Click the top “MLNLP” to select the “Starred” public account. Heavyweight content delivered first-hand. From:Number Theory Legacy The prerequisite knowledge for this article includes:Recurrent Neural NetworksRNN, Word EmbeddingsWordEmbedding, Gated UnitsVanillaRNN/GRU/LSTM. 1 Seq2Seq Seq2Seq is the abbreviation for sequence to sequence. The first sequence is called the encoder encoder, which is used to receive the source … Read more
Click on the above“Beginner’s Guide to Vision” to select “Star” or “Pin” Heavyweight content delivered first time From | Zhihu Author | Jixing Link | https://zhuanlan.zhihu.com/p/147885624 Editor | Deep Learning Matters WeChat Official Account This article is for academic exchange only. If there is any infringement, please contact us for deletion. Introduction In recent years, … Read more