Machine Learning Which Algorithm To Use

Learning methods depending on the type of data, there are different ways to model a problem. In the field of machine learning or artificial intelligence, people first consider the way of learning algorithms. In the field of machine learning, there are several main ways of learning. 　　It is a good idea to classify the algorithm according to the learning style, so that people can choose the most suitable algorithm according to the input data to get the best results when modeling and algorithm selection. Supervised learning: Under supervised learning, input data is called "training data", each group training number ...

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Machine learning is a combination of art and science. No machine learning algorithm can solve all the problems. There are several factors that can influence your decision to choose a machine learning algorithm.

Machine learning algorithm spicy, for small white I, the scissors are still messy, and I sort out some of the pictures that help me quickly understand. Machine Learning algorithm Subdivision-1. Many algorithms are a class of algorithms, and some algorithms are extended from other algorithms-2. From two aspects-2.1 learning methods supervised learning Common application scenarios such as classification problems and regression problems common algorithms include logistic regression (logistic regression) and reverse-transmission neural networks (back propagation neural netw ...

C + + computer vision ccv-based on C language/provides cache/core machine Vision Library, novel Machine Vision Library opencv-It provides C + +, C, Python, Java and MATLAB interfaces, and supports Windows, Linux, Android and Mac OS operating system. General machine learning Mlpack dlib Ecogg Shark Closure Universal machine learning Closure Toolbox-cloj ...

We compare deep learning with machine learning and discuss their differences in all aspects. In addition to the comparison of deep learning and machine learning, we will also study their future trends.

Machine learning is a multi-disciplinary subject that has emerged in the past 20 years and involves many disciplines such as probability theory, statistics, approximation theory, convex analysis, and computational complexity theory.

Machine learning is the most advanced aspect of the field of artificial intelligence today, and more beginners have begun to enter this field.

There are many articles on machine learning algorithms that detail the related algorithms, it is still very difficult to make the most appropriate choices.

There are quite a lot of routines for machine learning, but if you have the right path and method, you still have a lot to follow. Here I recommend this blog from SAS's Li Hui, which explains how to choose machine learning.