After getting used to the operations of Word2Vec, today we will lift the lid and see what it’s really like inside. Word2Vec can transform words into vectors, which sounds quite magical, right? But once you understand the principle, you’ll find it’s just a little trick of assigning mathematical labels to words. Core Idea: Prediction Is … Read more
This article will explain the essence of Back Propagation, its principles, and provide examples to help you understand Back Propagation in one read. Back Propagation 1. The Essence of Back Propagation Forward Propagation: Forward propagation is the process by which a neural network transforms input data into prediction results through its hierarchical structure and parameters, … Read more
1 Compiled by New Intelligence Source Author: Alberto Quesada Source: neuraldesigner.com Translated by: Liu Xiaoqin [New Intelligence Source Guide] There are thousands of algorithms for training neural networks. Which ones are the most commonly used, and which is the best? The author introduces five common algorithms in this article and compares them in terms of … Read more
Andrew Ng’s DeepLearning.ai Course Notes 【Andrew Ng’s DeepLearning.ai Notes 1】Intuitive Explanation of Logistic Regression 【Andrew Ng’s DeepLearning.ai Notes 2】Popular Explanation of Neural Networks Part 1 【Andrew Ng’s DeepLearning.ai Notes 2】Popular Explanation of Neural Networks Part 2 Is deep learning not working well? Andrew Ng helps you optimize neural networks (1) To improve the training efficiency … Read more
New Intelligence Report Source: deeplearning.ai Editor: Daming 【New Intelligence Guide】The initialization of neural networks is a crucial step in the training process, significantly affecting the model’s performance, convergence, and convergence speed. This article is a technical blog from deeplearning.ai, which points out that improper selection of initialization values can lead to problems such as gradient … Read more
Deep Learning is a major branch of Machine Learning, which attempts to use algorithms with multiple processing layers containing complex structures or multiple nonlinear transformations to perform high-level abstractions of data. Logistic Regression (Logistic Regression, also translated as “Log-Odds Regression”) is one of the discrete choice models, belonging to the category of multivariate analysis. It … Read more
Machine Heart reports Machine Heart Editorial Department With a theoretical foundation, we can perform deep optimization. Why is the performance of transformers so good? Where does the context learning (In-Context Learning) ability it brings to many large language models come from? In the field of artificial intelligence, transformers have become the dominant model in deep … Read more
Machine Heart reports Editors: Danjiang, Xiaozhou SVM is all you need; Support Vector Machines are never out of date. The Transformer is a new theoretical model of Support Vector Machines (SVM) that has sparked discussion in academia. Last weekend, a paper from the University of Pennsylvania and the University of California, Riverside, sought to explore … Read more
This article is a translated note of the Stanford University CS231N course, authorized for translation and publication by Professor Andrej Karpathy of Stanford University. The Big Data Digest work is prohibited from being reproduced without authorization; specific requirements for reproduction can be found at the end of the article. Registration is open! Machine Learning training … Read more
Follow the public account “ML_NLP“ Set as “Starred“, delivering heavyweight content to you first! Reprinted from | PaperWeekly ©PaperWeekly Original · Author｜Su Jianlin Unit｜Zhuiyi Technology Research Direction｜NLP, Neural Networks Although Transformer models have conquered most fields in NLP, RNN models like LSTM and GRU still hold unique value in certain scenarios, making it worthwhile for … Read more