Source fromMedium Author: garvitanand2 Compiled by: Machine Heart Contributors: Geek AI, Lu This article introduces the 10 most commonly used machine learning algorithms, including linear regression, Logistic regression, linear discriminant analysis, Naive Bayes, KNN, random forest, etc. 1. Linear Regression In the fields of statistics and machine learning, linear regression may be one of the … Read more
How to Obtain 1. Follow the public account below, and click 【Like】 and 【View】 in this article 2. Click 【Get Course】 in the public account to obtain this material There is a course on Basics of Machine Learning: Machine Learning and Materials/Chemistry Basics of Machine Learning: Machine Learning and Materials/Chemistry 1. Introduction to Machine Learning … Read more
1. Introduction: Inferring from Clues Hello everyone! Imagine you are a detective investigating a case. You would infer who the suspect is based on various “clues” left at the scene (such as fingerprints, footprints, eyewitness descriptions, etc.). The Naive Bayes algorithm acts like a “probability detective”; it predicts the likelihood of an event occurring based … Read more
In today’s digital age, machine learning is truly a hot topic. It acts like an intelligent assistant, helping us mine valuable information from vast amounts of data to make accurate predictions and decisions. With its simple and readable syntax and rich libraries, Python has become an excellent tool for implementing machine learning algorithms. Today, let’s … 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. This is a work from Big Data Digest, unauthorized reproduction is prohibited; specific requirements for reproduction can be found at the end of the article. Translation: Han Xiaoyang & Long Xincheng … Read more
Starting the Journey of RNN, Commonly Known Four Structures of RNN One to One: This is the traditional application of neural networks, usually used for simple input to output tasks. For example, in image classification, the network receives an image as input and identifies the category of the object represented in the image. Specifically, suppose … Read more
Hello everyone! Today I want to introduce an amazing AI tool—TensorFlow. How easy is it to build an image classification model with TensorFlow? Let me give you a few very practical examples: Scenario 1: Quickly Build a Simple Image Classification Model Suppose you have some images and you want the model to recognize which category … Read more
New Intelligence Column Author: Zhang Hao [New Intelligence Guide] The author of this article comes from the Machine Learning and Data Mining Institute (LAMDA) of the Computer Science Department of Nanjing University. This article systematically summarizes the applications of deep learning in four fundamental tasks in the field of computer vision, including image classification, localization, … Read more
This article is reprinted from the PaddlePaddle WeChat official account Editor’s Note: Written by a deep learning engineer from Baidu, this article provides a detailed overview of the eight major tasks in the field of computer vision, including: image classification, object detection, image semantic segmentation, scene text recognition, image generation, human keypoint detection, video classification, … Read more
Author: Ye Hu Editor: Tian Xu Introduction Recently, deep CNN networks like ResNet and DenseNet have significantly improved the accuracy of image classification. However, in addition to accuracy, computational complexity is also an important metric for CNN networks. Overly complex networks may be very slow, and specific scenarios, such as autonomous driving, require low latency. … Read more