Artificial intelligence has a troika: data, algorithms, and computing power. This article focuses on algorithm-related knowledge.

This article will introduce the concept of algorithm in artificial intelligence, 4 characteristics of algorithm, 6 general methods. And 3 points to note when choosing an algorithm.

## What is an algorithm?

Simply put, the algorithm is:**The means to solve the problem, and is a means of mass solving the problem.**

A recipe is an "algorithm", as long as you follow the recipe method, you can make the corresponding dish.

Algorithms in artificial intelligence are mainly used to train models.

机器 学习 There are 7 steps, and the third step is to choose the appropriate algorithm model. The final predictable model is obtained through training.

## 4 basic characteristics of the algorithm

The algorithm has the following four characteristics:

- feasibility
- Certainty
- Poor
- Have enough intelligence

For a detailed description of these 4 characteristics, please see the "Basic concepts of the algorithm"

## 6 basic methods of algorithms

Computer algorithms and human computing are different, and there are roughly six different ideas:

- Enumeration
- Induction
- Recursion
- Recursive
- Halving recursion
- Backtracking

For more details, check out the "Basic concepts of the algorithm"

## 3 Tips when choosing an algorithm

- Different algorithms may be used to solve different problems, and the same algorithm may be used.
**No algorithm is omnipotent**, but the scope of application is different. **The algorithm has no advanced and low level points.**The quick and cheap solution is the purpose, blindly pursue complex algorithms (for example: deep learning), equivalent to "using a cannon to fight mosquitoes"- Sometimes there are multiple algorithms that can solve the same problem, and it is the goal to solve the problem with the lowest cost and the shortest time.
**It is important to choose the right algorithm for your environment.**

## Baidu Encyclopedia + Wikipedia

The algorithm refers to the accurate and complete description of the solution, and is a series of clear instructions to solve the problem. The algorithm represents a systematic way to describe the problem-solving strategy. That is to say, it is possible to obtain the required output in a limited time for a certain specification input.

If an algorithm is flawed or not suitable for a problem, executing this algorithm will not solve the problem. Different algorithms may accomplish the same task with different time, space or efficiency. The pros and cons of an algorithm can be measured by space complexity and time complexity. The instructions in the algorithm describe a calculation that, when run, starts with an initial state and (possibly empty) initial input, passes through a series of finite and clearly defined states, and eventually produces an output and stops at a final state.

In mathematics and computer science, algorithms are a clear specification of how to solve a class of problems. The algorithm can perform calculations, data processing, and automatic reasoning tasks.

As an efficient method, algorithms can be expressed in limited space and time, as well as in well-defined formal languages for computational functions. Starting from the initial state and the initial input, the instruction describes a calculation that, when executed, ultimately produces an "output" and a final end state through a finite number of well-defined continuous states.

The concept of algorithms has existed for centuries. Greek mathematicians use algorithms in the sieves such as Eratosthenes to find prime numbers and use the Euclidean algorithm to find the greatest common divisor of two numbers. The word itself comes from the 9 century mathematician MuḥammadibnMūsāal-Khwārizmī, the Latinized Algoritmi. Partial formalization of the concept of modern algorithms began with an attempt to solve the Entscheidungsproblem (decision problem) proposed by David Hilbert in 1928. Subsequent formalization is defined as an attempt to define “effective computability” or “effective method”. These formalizations include the Gödel-Herbrand-Kleene recursive function for 1930 years, 1934 years and 1935 years, the lambda calculus for Alonzo Church for 1936 years, the Formula 1936 for Emil Post for 1, and the Alan Turing for 1936-37 and 1939. Turing machine.