Neural network is a set of specific algorithms. It is a kind of model in machine learning. The neural network itself is the approximation of general functions. It can understand how the brain works and can understand the parallel computing style inspired by neurons and adaptive connections. Solve practical problems, etc. by using brain-inspired novel learning algorithms.
Why do we need machine learning?Machine learning can solve the complex problems that humans can't directly deal with by programming. Therefore, we feed a large amount of data to the machine learning algorithm in order to get the desired answer.
Let's take a look at these two examples:
It is very difficult to write a problem-solving program, such as recognizing a three-dimensional object from a new perspective under new lighting conditions in a messy scene. We don't know how to solve this problem through code, because the recognition process in the brain is still unsolved for us. Even if we know what to do, the program to be written can be very complicated.
As another example, it is difficult to write a program to predict the probability of a credit card transaction fraud occurring, and there may not be any simple and reliable rules. Because fraud is a dynamic goal, the program needs to change. What we need to do is to combine a large number of weak rules to make fraud predictions.
Another is the machine learning method. Instead of writing a program for each specific task, we collect a large number of examples to specify the correct output for a given input. Then, the machine learning algorithm takes these examples and produces a program to do the job.
The program generated by the learning algorithm may look very different from a typical handwriting program. It may contain millions of numbers. If the method is right, the plan will apply to the new case and the case we trained. If the data changes, the program can also be changed by training the new data. You should note that a lot of calculations are now cheaper than paying someone to write a task-specific program.
Because of this, machine learning can solve some tasks well, including:
Pattern recognition: objects in real scenes, facial recognition or facial expressions, spoken language, etc.
Abnormal situation identification: abnormal sequence of credit card transactions, abnormal mode of sensor readings of nuclear power plants, etc.;
Forecast: Future stock prices or currency exchange rates, movies that one would like, etc.
What is a neural network?Neural network is a kind of model in general machine learning. It is a set of specific algorithms, inspired by biological neural networks, which completely changes the field of machine learning. The emergence of so-called deep neural networks has proven that it works very well.
Neural networks are themselves approximations of general functions, which is why they can be applied to almost any machine learning problem, because all machine learning is a complex mapping of learning from input to output space.
Here are three reasons to convince you to learn neural computing:
Can understand how the brain works: it's a very large and complex problem, and it's a question that has to be thought out of the way, so we need to use a computer to simulate.
Ability to understand parallel computing styles inspired by neurons and adaptive connections: this is a very different style than sequential computing.
Solving real problems by using brain-inspired novel learning algorithms: Even if this is not the way the brain actually works, such learning algorithms are also very useful.
In this article, I want to share eight neural network architectures that every researcher engaged in machine learning should be familiar with.
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