Friends familiar with deep learning know that LSTM is a type of RNN model that can conveniently handle time series data and is widely used in fields such as NLP. After watching Professor Li Hongyi’s deep learning videos from National Taiwan University, especially the first part introducing RNN and LSTM, I felt enlightened. This article … Read more
Abstract Neural networks need the right representations of input data to learn. Recently published in Nature Machine Intelligence, a new study examines how gradient learning shapes a fundamental property of representations in recurrent neural networks (RNNs)—their dimensionality. Through simulations and mathematical analysis, the study demonstrates how gradient descent guides RNNs to compress the dimensionality of … Read more
Principles of Neural Network Algorithms in Deep Learning Graphical Parameter Calculation Junior High Mapping Neural Network Mapping? What is a Neural Network Algorithm? Parameter Solving References What is a Neural Network Algorithm? Junior High Mapping In junior high school, we learned about mapping with the equation y = f(x). By using several pairs of values … Read more
In recent years, artificial intelligence has developed rapidly, gradually penetrating various industries and fields. More and more people are learning AI-related technologies. To help beginners quickly grasp the basic principles of AI, Professor Ma Shaoping, Vice Chairman of CAAI, has written an introductory book titled “How Computers Achieve Intelligence.” Through the new popular science column … Read more
Datawhale Insights Compiled by: Wang Xiaoxin, Source: Quantum Bit When adjusting the way models update weight 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 various optimization … Read more
About 8000 words, recommended reading time 20 minutes. This article introduces the mathematical principles behind artificial neural networks. Introduction When it comes to artificial intelligence algorithms, artificial neural networks (ANN) are an unavoidable topic. However, for beginners, it is often easy to be overwhelmed by the complex concepts and formulas in ANN, leading to a … Read more
Author: Matthew Stewart Translator: Che Qianzi Proofreader: Chen Dan This article is approximately 5500 words, and it is recommended to read it in 12 minutes. The knowledge in this article will provide a strong foundation to introduce you to the performance of neural networks, applied in deep learning applications. “Your brain does not generate thoughts. … Read more
Click the above “Little White Learns Vision“, choose to add “Starred” or “Top“ Heavyweight content delivered first-hand The optimization of machine learning (objective) can be simply described as: searching for a set of parameters w for the model that can significantly reduce the cost function J(w). This cost function usually includes performance evaluation over the … Read more
Datawhale Insights Compiled by: Wang Xiaoxin, Source: Quantum Bits When adjusting the model’s weight and bias update methods, 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 various optimization algorithms … Read more
Click the "Advanced Programming" above and select the "Star" public account Super valuable content delivered to you immediately!!! In this article, we will demonstrate how to build a simple three-layer neural network from scratch. Although we will not derive all the mathematical operations involved in detail, I will do my best to explain our approach … Read more