:) People watching and enjoying these videos makes it so much more fun to make them. So indeed, thanks for watching!

@@Eigensteve ..being happy to see other people making progress. Man, you have a great heart..!

Steve, we should be thanking "you"

Presenter with true class 👏

😁😍

It was UKposts's turn in the introduction round

I hope Jus relu and sigmoid

@@GowthamRaghavanR those are the safe ones

Imagine for a second also what the algorithm never recommended to you, because it already knew you were aware.

@@resinsmp Now that's an interesting thought haha. "Since user searched this type of topic, it must already be aware of some other certain type of topics." Simply marvelous!

This video has common variables with other videos you watch!

Sounds like you’ve been autoencoded

That's why the CF algorithm did

same thing

UKposts also uses neural networks

John, I recommend Stanford's course on recurrent neural networks. Free on UKposts. It's a playlist with over 20 lectures

ukposts.info/slow/PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

shut up

profound observation

Thank you for your comment, excellent observance!

I dig that perspective. I do think that compression can have some downsides. I feel like my emotional reactions to things are a sort of "compression". I can't keep track of everything I've read about a potentially political topic, but I can remember how it made me feel.

I like to think of autoencoder as an architect outputting a blueprint, then a construction company building that building

Also loved the graphic style. We're the images projected on a screen in front of you? Great result, I wish more people showed info this way

So cool! Which video?

YES. ☝️this

Well, that makes sense given he's a renowned professor =)

Glad you enjoy it! Thanks!

Human brain

@@bunderbah Bruman hain

@@MilaPronto Humain bran

one hot encoder. lols

@@mbonuchinedu2420 That's like a robot trying to be funny

Hahaha. Good.

It's becoming sentient! Even worse, it's a teenager who just wants to be understood. XD

Thanks so much!

@@Eigensteve OMG it's my honor😯. I didn't expect you would read my comment lol. I hope I could get in to UW this fall so that I could be in your class in person.

A neuron is pure software, a computational unit that mimics the basic functions of a biological neuron. While software relies on specific hardware for execution, a neuron is not a simple server. Unlike an Excel cell, which takes a single input and produces a straightforward output, a neuron receives multiple inputs from other neurons, processes them, and generates an output based on the combined information. Each input to a neuron is multiplied by a weight, a numerical value that represents the strength of the connection between the neurons. These weighted inputs are then summed together, and a bias value, representing an inherent offset, is added to the result. The resulting value is then passed through an activation function, which introduces non-linearity into the network's decision-making process. Activation functions, such as sigmoid and ReLU, transform the weighted input into the neuron's output, allowing the network to capture complex patterns and relationships in the data. ReLU is often used as an activation function because it requires less computational power compared to other activation functions, such as the sigmoid function. Through a process called learning, artificial neurons adjust their weights over time, enabling the network to improve its performance on a given task. Algorithms like back propagation guide this learning process, allowing the network to minimize errors and optimize its decision-making capabilities. Hope this helps.

Glad you enjoy it!

Thanks!