81–86 of 86 posts
I wish that the tutorial went just one more step. It presents a one dimensional perceptron. But most perceptrons are multi-input. Adapting the article's 1D perceptron to three-input, for example: import random learning_rate = 0.1 EPOCHS = 50 NUM_INPUTS = 3 weights = [random.uniform(-1, 1) for _ in range(NUM_INPUTS)] bias = random.uniform(-1, 1) data = [] for _ in range(100): inputs = [random.uniform(-1, 1) for _ in r…
I have still so many questions left, but regardless of that it was a great read. Thanks for sharing!
Shameless plug of my own interactive version of this (ai assisted, but probably not slop) https://sourceobscure.com/perceptron/
I can build a smaller brain. f(x) = 0.
This brain is interesting. Basically you get a no for everything you ask, right?
Earlier quoted context omitted.
The first AI winter was largely triggered by Minsky in a book he published in 1969, which mathematically proved that single-layer perceptrons couldn't solve non-linear problems. Favorite quote: "Our intuitive judgment is that the extension [to multilayer systems] is sterile." Yet we had the computational power to run backpropagation in the 1960s and small Transformers in the 1970s (I'm the author of both): https://gi…
I wonder had we invented transformer architecture back in the 70's or 80's, if the pace of hardware innovation would have naturally slowed AI progression, and given humans decades to slowly adapt, rather than the current tidal wave (that seems to grow in size daily) bearing down on us.
I'll add this to my casual mental list of "Technologies that could have been developed generations earlier but through random chance, simply weren't."
If you want to learn the fundamentals of ML I recommend a book, such as Deep Learning: Foundations and Concepts by Chris Bishop. If you insist on staying online, one option is https://course.fast.ai/ If you don't know ML I don't think you're going to learn much through ad hoc demos.