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The Smallest Brain You Can Build: A Perceptron in Python

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Re: The Smallest Brain You Can Build: A Perceptron in Python

#25
post #3

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.

I didn't know Bishop had released a new textbook. I will have to take a look at it. I wasn't the biggest fan of his Pattern Recognition book as I found it overly dense. I much preferred the Murphy and Alpaydin books. EDIT: His son is co-author?

I still find his pattern recognition book useful and informative. It may be dense, but some of us consider that a positive for 'reference' literature. That book was one of very few that still holds up well fr when it was published - truly in on of the last "dark ages" of ML.

I think those down voting you are perhaps overly eager. I upvoted. Grab "Deep Learning" - you'll find it useful, imteresting, and likely less 'dense' in the negative sense!

Re: The Smallest Brain You Can Build: A Perceptron in Python

#26
The IF statement is the root creator of software programming. It has the ability to compare two values against each other and branch out to blocks of instructions. So it is perceiving (reading), decision making and routing - all that which differentiate life from inanimate objects. The AI agents perform the exact same loop, by delegating the first two steps to a model.

Going further backwards, the transistor (or a PNP junction) is the hardware level enabler of the IF statement. The action (switching) driven by the current which in turn controls other switches, is the first manifestation of "observe and act" by inanimate things at the speed of electricity.

Mechanical equivalents existed ofcourse - speed of a governer which controls the flow of fuel which in turn controls the speed of the governer.

Re: The Smallest Brain You Can Build: A Perceptron in Python

#28
post #25

Earlier quoted context omitted.

I didn't know Bishop had released a new textbook. I will have to take a look at it. I wasn't the biggest fan of his Pattern Recognition book as I found it overly dense. I much preferred the Murphy and Alpaydin books. EDIT: His son is co-author?

I still find his pattern recognition book useful and informative. It may be dense, but some of us consider that a positive for 'reference' literature. That book was one of very few that still holds up well fr when it was published - truly in on of the last "dark ages" of ML. I think those down voting you are perhaps overly eager. I upvoted. Grab "Deep Learning" - you'll find it useful, imteresting, and likely less 'd…

Appreciate your comment. I skimmed the online version and it covers all the 2010s era developments all the way to Transformers which is enough to earn it a spot on my bookshelf.

> Grab "Deep Learning" - you'll find it useful, imteresting, and likely less 'dense' in the negative sense!

Absolutely! I just ordered it and it's enroute :)

Re: The Smallest Brain You Can Build: A Perceptron in Python

#29
In the early days of machine learning (before the first AI winter), networks like this were often implemented and trained in hardware: https://en.wikipedia.org/wiki/ADALINE

That was the first thing that came to mind when I read "the smallest brain you can build". Nowadays, that "small brain" would likely be built on a breadboard using op-amps instead.

Re: The Smallest Brain You Can Build: A Perceptron in Python

#30
post #24

> A perceptron *is* the smallest brain you can build. > In 1958, a researcher named Frank Rosenblatt built a machine *he called* the perceptron. > It was *inspired* by a single brain cell, a neuron.

Yes . But at least the post seems to be written by OP himself!

and its an a great learning resource - which is arguably more important :-)

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