Click on 'Xiaobai Learns Vision' above, select to add 'star' or 'top' Important content delivered first Introduction This article analyzes the reasons for model training not converging or failing from both data and model perspectives. Four possible reasons from the data aspect and nine possible issues from the model aspect are summarized. In addition, the … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers.The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted from … Read more
This article is about 8000 words long and is suggested to be read in 16 minutes. This article provides a detailed introduction to the relevant content from Graph to Graph Convolution. The author has recently reviewed several papers on Graphs and Graph Convolutional Neural Networks (GCNs) and is deeply impressed by their power. However, some … Read more
Welcome to click the blue words to follow “Smart Journal IT“! Zhang Chenglong, Ding Shifei, Guo Lili, Zhang Jian Journal of Software Journal of Software Abstract The stochastic configuration network (SCN) is an emerging incremental neural network model. Unlike other randomized neural network methods, it can configure hidden layer node parameters through a supervisory mechanism, … Read more
When adjusting the way the model updates weights and bias parameters, have you considered which optimization algorithm can yield better and faster results for the model? Should you use gradient descent, stochastic gradient descent, or the Adam method? This article introduces the main differences between different optimization algorithms and how to choose the best optimization … Read more
Click the above “Beginner’s Visual Learning” to select to add Star Mark or “Pin” Essential content delivered in real-time In this article, the author visualizes 26 activation functions including ReLU and Sigmoid, along with relevant properties of neural networks, providing a great resource for understanding activation functions. In neural networks, the activation function determines the … Read more
Author: Yu Yu Lu Ming Editor: Peter Hello everyone, I am Peter~ There are many doubts about the number of hidden layers and neurons in neural networks. I happened to see an article that answered these questions well, and I’m sharing it with you~ https://zhuanlan.zhihu.com/p/100419971 1. Introduction BP neural networks are mainly composed of input … Read more
Standard Convolution 1. Background of Convolution 2. Convolution Kernel / Feature Map / Convolution Calculation 3. Padding 4. Stride 5. Receptive Field 6. Multiple Input Channels, Multiple Output Channels, and Batch Operations 7. Advantages of Convolution 8. Example Applications of Convolution References 1×1 Convolution 1. 1×1 Convolution 2. Example Applications References 3D Convolution 1. 3D … Read more
Selected from | mlfromscratch Author | Casper Hansen Source | 机器之心 Contributors | 熊猫、杜伟 The importance of activation functions in neural networks is self-evident. Casper Hansen from the Technical University of Denmark introduces the sigmoid, ReLU, ELU, and the newer Leaky ReLU, SELU, and GELU activation functions through formulas, charts, and code experiments, comparing their … Read more
This article shares an analysis of the mathematical principles behind CNNs, which will help you deepen your understanding of how neural networks work in CNNs. As a suggestion, this article will include quite complex mathematical equations. If you’re not accustomed to linear algebra and calculus, that’s fine; the goal is not to memorize these formulas … Read more