Article directory Preface 1. Loading of data sets 2. Divide into training set and test set, shuffle operation, two classifications 3. Training model and prediction 4. Model Evaluation 1. Cross-validation 2.Confusion Matrix-confusion matrix 2.1 Precision,recall,f1_sorce 2.2 The impact of ROC curve and threshold on the results 5. Summary Foreword Earlier we introduced common methods and […]
Tag: machine-learning
Machine Learning model development on Azure cloud workstation – full process demonstration
Directory Contents of this article prerequisites Start with “Notebook” Set up a new environment for prototyping (optional) Create notebook Develop training scripts Iterate test result Follow TechLead and share all-dimensional knowledge of AI. The author has 10+ years of Internet service architecture, AI product development experience, and team management experience. He holds a master’s degree […]
[Machine Learning][Part 6]Cost Function cost function and gradient regularization
Table of Contents fitting Underfitting overfitting correct fit Methods to solve overfitting: regularization Both linear regression models and logistic regression models suffer from underfitting and overfitting. Fitting Explanation from Baidu: Data fitting, also known as curve fitting, commonly known as curve drawing, is a method of substituting existing data into a mathematical expression through mathematical […]
02 Machine Learning Algorithm Linear Regression Algorithm
Linear regression algorithm of machine learning algorithm Table of Contents 1. Introduction to linear regression algorithm 2. Implementation of simple linear regression algorithm 2.1 Implementation by for loop 2.2 Implementation by vectorization 3. Indicators for measuring linear regression algorithms 4. The best indicator to measure linear regression method is R Squared 5. Multiple linear regression […]
[Machine learning][Part4] Implementation of gradient descent linear prediction model under multi-dimensional matrix
Table of Contents Model initialization information: Model implementation: Multivariable loss function: Multivariable gradient descent implementation: Multivariable gradient implementation: Multivariable gradient descent implementation: The training example in the previously partially implemented gradient descent linear prediction model has only one feature attribute: house area, which is obviously not in line with the actual situation. Here, increase the […]
[Machine Learning] Pytorch hand-rolls a neural network model
Because although I have written a little bit about pytorch before, it is still used too little. This time I started from scratch and tried to create a neural network model. (Because there is no training data, the final training part uses code that may not be easy to run as a demonstration. If you […]
Software Engineering Patterns for Designing Machine Learning Systems
(((system OR software) AND machine learning) AND ((implementation pattern) OR (pattern) OR (architecture pattern) OR (design pattern) OR (anti-pattern) OR (recipe) OR (workflow) OR (practice) OR (issue) OR (template))) WN ALL) + ((cpx OR ins OR kna) WN DB) AND (({ca} OR {ja} OR {ip} OR {ch}) WN DT) This expression describes a condition filter […]
An Automatic Hyperparameter Optimization Strategy for Machine Learning Models
Abstract Machine learning models are often sensitive to hyperparameters, which can significantly affect their performance. In this paper, we propose an automatic hyperparameter optimization strategy that aims to efficiently search for the optimal combination of hyperparameters. Our approach combines multiple techniques, including random search, grid search, and Bayesian optimization, to explore the hyperparameter space and […]
Using machine learning to identify patterns in scientific
Author: Zen and the Art of Computer Programming 1. Introduction This article focuses on how to use machine learning methods to identify patterns in scientific data. Machine learning has been a very popular research direction in recent years and can be used to solve many practical problems. Since scientific data are usually high-dimensional, complex, and […]
Machine Learning for Programming Languages: A Practical
Author: Zen and the Art of Computer Programming 1. Introduction Computer programming language research is a highly complex field, and the technologies involved include compilers, interpreters, virtual machines, editors, debuggers and many other aspects. In order to better improve the efficiency and maintainability of computer programming languages, machine learning methods are widely used in the […]