Reinforcement Deep Learning

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 ...

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.

In the past decade, there has been a surge in interest in machine learning. Almost every day, we can see discussions about machine learning in a variety of computer science courses, industry conferences, the Wall Street Journal, and more.

This paper raises objections to this view, thinking that machine learning ≠ data statistics, deep learning has made a significant contribution to our handling of complex unstructured data problems, and artificial intelligence should be appreciated.

At the heart of machine learning is "using algorithms to parse data, learn from it, and then make decisions or predictions about something in the world." This means that instead of explicitly writing a program to perform certain tasks, it is better to teach the computer how to develop an algorithm to accomplish the task.

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The most important algorithm is the neural network, which is not very successful due to overfitting (the model is too powerful, but the data is insufficient). Still, in some more specific tasks, the idea of using data to adapt to functionality has achieved significant success, and this also forms the basis of today's machine learning.

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

Open source machine learning tools also allow you to migrate learning, which means you can solve machine learning problems by applying other aspects of knowledge.