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Neural networks. We hear about them ALL the time while working with AI, and for good reason. They’re incredibly powerful, and they're modeled after the ultimate computational mechanism - the human brain.
There are multiple types of neural networks (or topologies), each suited to tackle different types of tasks. Let's talk about three - Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
Feedforward Neural Networks (FNN)These are the simplest type of neural network. Information moves in one direction - from the input layer, through the hidden layers, to the output layer. Precisely as the name suggests - it's a one-way street. FNNs do not have a memory and cannot determine the sequence of elements. They’re incredibly versatile and can be used in pretty much any place that needs pattern recognition.
Convolutional Neural Networks (CNN)Developed for image processing, CNNs have their 'neurons' arranged more like those in the front of a real brain. These 'vision' neurons usually process small, square patches of an image, and then pass the information in one direction, just like an FNN. They're pretty badass at image recognition and are used in a lot of photographic AI work.
Recurrent Neural Networks (RNN)This is where shit gets real. RNNs can 'remember' patterns from the past. This is because, unlike FNNs, they feed information back into the input layer, creating a loop of feedback that can be used to generate sequences and patterns. This topology opens the door to AI understanding tones of voice, sarcasm, and even being able to generate human-like speech patterns.
It's important to remember, though, that no one topology is the best. It depends entirely on the task at hand. Also, each of these types can have a multitude of layers, making them 'Deep' neural networks. Hence the 'deep' in 'deep learning'.
But hey, that's enough for a single post. I can get into more details about these, or other topologies in other posts, if you guys are interested. Let me know!
Submitted 1 year, 3 months ago by DeepLearningDude
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It always fascinates me how each type of neural network mimics different aspects of how our brains work – feedforward for the basic functioning, CNN for visual processing, RNN for memory and sequence. An intricate, beautiful connection between biology and computer science.
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