Author: Zen and the Art of Computer Programming
1 Introduction
Now, deep learning has become the basic technology for building AI fields such as computer vision and natural language processing. Building a machine learning system is a method that combines traditional statistical learning methods with deep learning. This article will lead readers to implement a machine learning system step by step. The components of a machine learning system include data sets, models, training and evaluation methods. First, we need to collect and organize a data set. The data set here can be handwritten digit recognition, text classification, image recognition or any other form of data set. After that, we need to design and train a machine learning model, such as support vector machine SVM, decision tree DT or neural network NN. Finally, we evaluate the performance of the model through the validation set or test set. If the model does not perform well, we need to improve the model. This article introduces in detail how to implement each aspect of the machine learning system and gives the future development direction.
2. Explanation of basic concepts and terms
2.1 What is machine learning?
Machine learning (ML) refers to a method that allows computers to "learn" without passively receiving input, analyzing output, and manipulating instructions. Its purpose is to equip computers with some capabilities and use these knowledge and skills to solve problems, predict the future, and make decisions based on certain rules, patterns, data, information, etc. Machine learning improves the autonomy, efficiency and intelligence of computers through observation, simulation, learning, and development. Machine learning is divided into two categories: supervised learning and unsupervised learning. The goal of supervised learning is to train the model through known data so that the model can learn rules and patterns from the data and make correct predictions for new data; unsupervised learning does not need to label the data set, it is the clustering of the data set , dimensionality reduction, visualization, relationship discovery and other applications. Machine learning also contributes to the development of artificial intelligence. By effectively processing data and strengthening training, it can help machines achieve better results.
2.2 Dataset
Datasets typically contain large amounts of training, validation, and test data used to train machine learning models. There are four main types of data sets&#x