Implementing a Neural Network from Scratch in Python

Implementing a Neural Network from Scratch in Python

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

Understanding Deep Learning: Basics of Artificial Neural Networks

Understanding Deep Learning: Basics of Artificial Neural Networks

Reprinted from Yunqi Community as required ID: yunqiinsight Author | doflamingo Introduction I have touched on deep learning during my studies, but only superficially. In this era of data and algorithms, it is necessary to get closer to the algorithms. Therefore, from the perspective of an engineer, I hope to record the basics of deep … Read more

Word2Vec Python Source Code Analysis

Word2Vec Python Source Code Analysis

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

Understanding Back Propagation in Neural Networks

Understanding Back Propagation in Neural Networks

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

Five Major Algorithms for Training Neural Networks: Technical Principles, Memory and Speed Analysis

Five Major Algorithms for Training Neural Networks: Technical Principles, Memory and Speed Analysis

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

Optimizing Neural Networks: Techniques and Methods

Optimizing Neural Networks: Techniques and Methods

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

Understanding Neural Network Initialization

Understanding Neural Network Initialization

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

Intuitive Explanation of Logistic Regression by Andrew Ng

Intuitive Explanation of Logistic Regression by Andrew Ng

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

Where Does the Context Learning Ability of Transformers Come From?

Where Does the Context Learning Ability of Transformers Come From?

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

Transformers as Support Vector Machines

Transformers as Support Vector Machines

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