You can combine Genetic Algorithms and Neural Networks to evolve simple neural configurations, such as Neural Networks that perform logic operations (including the phantomatic XOR!).
This is a topic I very much like because - if you think about it - it's a bare bones model of how our brains evolved (I am not saying we have logic gates in our head).
Here are some problems that I think feed forward neural nets (with multiple hidden layers) might be able to solve.
Given the number of packets sent/recieved on the network interface, the volume of ambient noise, and the level of ambient light, attempt to predict the time of day.
Given a latitude and longitude, attempt to predict the elevation, or crime rate.
Given some simple metrics about the keywords in the title of an article, predict how many upvotes it has.
Given the digits of a random phone number, predict where the line terminus is located.
This is more challenging: visualize (ie, plot) the decision boundary surface of a 2-layer neural network. (With 1 layer the boundary is linear, so it's easy).
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您可以结合遗传算法和神经网络来演化简单的神经配置，例如执行逻辑运算的神经网络（包括幻象异或！）。
这是我非常喜欢的一个话题，因为 - 如果你仔细想想 - 这是我们大脑如何进化的一个简单的模型（我并不是说我们的大脑中有逻辑门）。
它很简单——而且应该很有趣！
You can combine Genetic Algorithms and Neural Networks to evolve simple neural configurations, such as Neural Networks that perform logic operations (including the phantomatic XOR!).
This is a topic I very much like because - if you think about it - it's a bare bones model of how our brains evolved (I am not saying we have logic gates in our head).
It is simple enough - and should be good fun!
从更广泛的角度来看，所有涵盖模式识别和信号处理的内容都可以充分利用神经网络。
此外，您还可以使用神经网络来开发游戏（策略、足球游戏）的“伪人工智能”。
无论如何，神经网络不仅仅是一种“解决方案”，更是一种工具，它可以用于经济学、物理学、导航、信号处理等领域。
此外，存在多种类型的神经网络（感知器、hopfield），关键是要使用他们根据问题明智地解决问题。
神经网络不是万能药，只是一个（非常有趣且强大的）工具。
In a wider way, all that cover pattern recognition and signal processing could take great advantage of neural networks.
Also, you could use neural networks to develop "pseudo-AI" for games (strategy, soccer games).
Anyway, as neural network is a tool more than a "solution", it can be used in economics, physics, navigation, signal processing, etc.
Also, many types of neural networks exist (perceptron, hopfield), the thing is to use them wisely according to the problem.
Neural networks are not panacea, just a (very interesting and powerful) tool.
人脸识别呢？
what about face recognition?
以下是我认为前馈神经网络（具有多个隐藏层）可能能够解决的一些问题。
Here are some problems that I think feed forward neural nets (with multiple hidden layers) might be able to solve.
这更具挑战性：可视化（即绘制）2 层神经网络的决策边界表面。 （对于 1 层，边界是线性的，所以很容易）。
This is more challenging: visualize (ie, plot) the decision boundary surface of a 2-layer neural network. (With 1 layer the boundary is linear, so it's easy).