Ask most fundamentals of artificial neural networks pdf if they want a brain like a computer and they’d probably jump at the chance. But look at the kind of work scientists have been doing over the last couple of decades and you’ll find many of them have been trying hard to make their computers more like brains! Photo: Computers and brains have much in common, but they’re essentially very different.
What happens if you combine the best of both worlds—the systematic power of a computer and the densely interconnected cells of a brain? You get a superbly useful neural network. How brains differ from computers You often hear people comparing the human brain and the electronic computer and, on the face of it, they do have things in common. Neurons are so tiny that you could pack about 100 of their cell bodies into a single millimeter. That’s where the comparison between computers and brains begins and ends, because the two things are completely different.
It’s not just that computers are cold metal boxes stuffed full of binary numbers, while brains are warm, living, things packed with thoughts, feelings, and memories. The real difference is that computers and brains “think” in completely different ways. Wouldn’t it be great if computers were more like brains? That’s where neural networks come in! Computer chips are made from thousands, millions, and sometimes even billions of tiny electronic switches called transistors. That sounds like a lot, but there are still far fewer of them than there are cells in the human brain. The amazing thing about a neural network is that you don’t have to program it to learn explicitly: it learns all by itself, just like a brain!
What does a neural network consist of? The higher the weight, the more influence one unit has on another. This corresponds to the way actual brain cells trigger one another across tiny gaps called synapses. Inputs are fed in from the left, activate the hidden units in the middle, and make outputs feed out from the right.