Regarding the calculation of RNN parameters, the PPT does not explain it very clearly, as it only contains images without text. At the same time, the textbook version by Qizhi Yao does not provide any exercises related to RNN parameter calculation, and the final exam may only test based on the descriptions in the images, … Read more
Selected from arXiv Compiled by Machine Heart Contributors: Jiang Siyuan, Smith, Li Yazhou Recently, a paper titled “Self-Normalizing Neural Networks” published on arXiv has garnered significant attention in the community. It introduces the Scaled Exponential Linear Unit (SELU), which brings in a self-normalizing property. This unit mainly uses a function g to map the mean … Read more
Click to follow the accounting master, and master accounting practices! The most commonly used lookup function in Excel is the Vlookup function. If you encounter more complex situations, you can use the Lookup function, but there is one case where both of these functions can only stand by, which is the lookup in a Pivot … Read more
Produced by Big Data Digest Author: Jiang Baoshang Many students, after starting with machine learning, directly use TensorFlow to implement neural networks, with little understanding of the underlying mechanisms of neural networks. Programming languages and frameworks evolve rapidly, so understanding the principles behind them is essential. Below, we will implement a neural network step by … Read more
This article will cover the essence of Softmax in terms of its principle and applications, helping you understand the Softmax function in one go. Softmax Activation Function 1. Essence of Softmax Essence Softmax is generally used as the last layer in a neural network for output in multi-class problems. Its essence is an activation function … Read more
Neural Networks are inspired by the operation of biological neural networks. Artificial neural networks are typically optimized through a learning method based on mathematical statistics, making them a practical application of statistical methods. Like other machine learning methods, neural networks have been used to solve a variety of problems, such as machine vision and speech … Read more
This article is a note from Andrew Ng’s DeepLearning.ai course, covering the content of neural networks. The previous part can be found in[Simple Explanation of Neural Networks Part One] 4Activation Function When building a neural network, one important question to consider is which activation function to use for each independent layer. In logistic regression, the … Read more
Recently, researchers from Nanyang Technological University published a paper that describes the mathematical principles of convolutional networks. This paper explains the operations and propagation processes of convolutional networks from a mathematical perspective. It is very helpful for understanding the mathematical essence of convolutional networks and aids readers in implementing convolutional networks “from scratch” (without using … Read more
Selected from arXiv Translated by Machine Heart Contributors: Huang Xiaotian, Lu Xue, Jiang Siyuan Recently, researchers from Nanyang Technological University published a paper describing the mathematical principles of convolutional networks. This paper explains the entire operation and propagation process of convolutional networks from a mathematical perspective. It is very helpful for understanding the mathematical essence … Read more
Question: I have searched various materials and read the original papers, which detail how Q, K, and V are obtained through certain operations to derive output results. However, I have not found any explanation of where Q, K, and V come from. Isn’t the input to a layer just a tensor? Why do we have … Read more