How Does a Neural Network Work in 60 seconds? The BRAIN of an AI 

That is what I thought. He'd make us pros

It’s because he is simplifying big time , yo start it’s a vector equation, and then the training and the network construction are the funny part.

​@@jaimeduncan6167 I wann a know more about it

😂

Question: what is the reason that (numerical) "solving for weights and biases" is *possible* in practice in the case of a larger ANN? And that with a simple gradient descent...!? An ANN model has 10^4 to ^9 parameters, ie the equation has that many Variables... In case of nonlinear systems, one would be *very* lucky to get a solver algorithm that delivers good results. Is there a deeper conceptual answer why this works with coupled perceptron model- equations?

@@jbruck6874 Because they don't just use gradient descent they also have extra optimizers too. For why they generalize and don't just overfit to everything, we don't know. We just know that Convolutional NNs converge onto interpretable image features. And that reverse engineering sentiment Recurrent NNs reveal line attractor dynamics, and converge to simpler representations than theoretically possible

There is one principle in the world, 1) Everything works on supply and demand. And also neural network use atomic abstractions that create a fluid essence in which the abstractions of outer world can be absorbed and reflected.

So what is the activation function and how to combine with this one, in simple terms?

Yes it's more complicated of course but this is the basic formula. Determining w and b is where you need crazy math lol

Same Lmao

y = mx + b.....but applied to a large ass matrix. He oversimplifed it because the training phase is very iterative and computational intensive.

I wanna know more about it​@@lidarman2

you right i think

Yea sigmoid function is used to map the value you get from linear function to range between [0,1] which is used as activation value

no? it does learn as it changes the weight and bias

@@caldeira_a It's not capable of doing that when it's running though and the pace of witch one learns is very slow. They adapt they don't learn. It effectively a much more advanced version of a decision tree. Liquid neural nets and spiking neural nets come much closer to learning however but we do not use those as they are more difficult to control. Also convolutional neural nets are not temporally aware and they can't think as they are built to be very linear. True learning involves taking new data understanding it by using previous data and then applying the new data in a way that is appropriate to context. Convolutional neural nets only do 50% of this as they can understand new data with existing data but they can't really act on it much with the weights being changed witch doesn't happen with the net alone and learning would also imply a capacity for multiple tasks witch a convolutional neural nets cannot do well as a consequence to there vary linear design. Transformers are better than convolutional neural nets but they have mostly the same problems. A liquid nureal network and spiking neural network can adjust there own effectives and learn autonomously with being retrained and they constantly retrain themselves like biological nureal network.

@@mrspook4789 at this point you're just using semantics, the process where you say it "adapts" it isn't just adaptation, it takes in its mistakes and attempts to correct them, increasing it's own accuracy. Sure, it may not be self aware and thus not be straight up literal artificial intelligence but it's learning nonetheless

@@caldeira_a no it isn't. That's like saying that a computer learned a new task If you reprogram it to do something completely different. Traditional neural nets cannot "learn" on their own that mechanism is done externally. For example a few companies once sent several chatbots into social media apps as an experiment to watch them "learn" and technically a chatbot knows rights from wrongs as that is within its knowledge however those chatbots became racist anyways and the reason because of this is because they're programming was altered due to the statistical patterns of language it was receiving but if it were never retrained it would have never became racist. They don't learn they are just adapted to serve a function and the way that works is through back propagation were you already have the answer and you send the answer back through the neural net in a way that changes the weights and biases literally rewriting the neural nets code to best match the answer and in that case with the chatbot the answer was a bunch of racism. Learning requires you to be aware of previous events and of what you are receiving and the ability to act upon it and convolutional neural nets do not do this neither do Transformers however transformers can do something kind of close to learning. Transformers are often equipped with programs that give them short-term memory that allow them to look at several sentences of text and generator response based on context in this even allows The transformer AI to learn within the extent of its own short-term memory however the training data is not changed it will always have the same behavior and a short-term memory is not unlimited which means things learned within short-term memory will eventually be lost as the training data will prevail as that is permanent. This is where liquid neural nets, spiking neural nets and biological neural nets like brains come to shine because there training data and memory and experience are completely the same but with a transformer and convolutional neural net they are completely separate.

Bruh

@@WeyardWiz bruh what 🤔

@@timmygilbert4102 We have no idea what you just said

@@WeyardWiz that's sad, it's English. The formula of a neuron is sum of inputs x weight, then the result is added to a bias value, and submit to the activation function that does a thresholding, IE it activate if the sum is above a value defined by the bias. So the original multiplication is simply filtering the input, IE multiplication by zero remove the contribution of that input, by one it let pass the input value unchanged. Thus only relevant value are taken into account. The sum is basically telling how strong of a signal we have from the input after filtering. The bias shift the sensibility up or down before the activation function. If the signal after bias is strong enough, the activation function trigger it's output to be further processed in the next layer as input. If the bias is low, the signal don't have to be strong, even a single input passing through the filtering will trigger the neuron. IE similar to OR logic. But if the bias is high, all input filtered need to be high, IE the signal need to be strong to activate the neuron. That's equivalent to AND logic. Any bias between low and high create a spectrum between these two logic.

@@timmygilbert4102 Well that's much more thorough and easier to grasp, thnx