Components of Neural Network|Neural network Weight, Bias, layers, activation

Clear as blue sky! Thanks

Does each neuron in a specific hidden layer have the same activation function?

can we take bias except 0&1 ?

Very clearly explained! Thanks to you!

Sir, how can we change the activation function via Backward Propagation?

Hi, can you tell what is the range of the weights.

All the neurons in a convolution layer have different Weights and Biases.? please anyone reply to it. Because when i searched in Google & Chatgpt i got different answer. Is biases is same or different?

One of the clearest explanations on this I've ever seen! You have a real talent for simplifying complexities and communicating them.

I am sorry to say thi but this is not what i have studied. How can you add the weights between your dataset attributes and input layer? The first connections (which have weights i.e. the first weights) arrive between the neurons of the input layer and first hidden layer.
This is not what you have explained.

Can we say weight is same as slope?

Thank you for making this video.

Best explanation ever seen sir

Excellent presentation. Good job.

saaf saaf hindi me kio nhi batate

Aur chota bord lele bhai

Great !!!

Trying to get into the basics but i didn't get any till i found u aman thank you so much

I have been watching videos in 3Bleu1Brown channel for last couple of days and trying to understand how neural network works but a lot of jargons clouded my understanding so much that i failed to catch this simple operation. You have nailed it, man. May Allah bless for making my day. So grateful, Sir.

you are the best man

You did not explain Bias

thank you very much!

Best😭❤️❤️❤️❤️

Hey, this was great. Thank you!

Hi Aman , In your videos , you have showed the example of one neuron with random weights and bias . would the weight will same for the other neurons in the hidden layer ?

Hi Aman , Great job !! :)
Can you also add all the deep learning algorithms as well such as RNN , CNN , LSTM and conv 2d ?

I saw some models using weights and biases between 0 and 1 and some -1 and 1. Is there any difference or advantages of using one over the other?

Awesome sir

Awesome explanation🥰😍

Thanks

May I have the link of the video about how to assign weights?

I've been looking for a love button amongst the like buttons 🥰. Your explanations are so simple and understandable! Thank you

okeh

Thank you for the explanation!

Damn you are so good

very well explained

if he was born with american accent he would give lessons at MIT
Nice explanation

very good video

how to calculate bias value?

That's why we need to perform training in order to find the appropriates weights and bias, training a neural network is basically trying how many times the neuron sees the given datasets, and then will do the gradient backward propagation to find the values of the weights and biases. If you try to explore what's inside a pre-trained neural network model for example, what you will see there is only a bunch of those weight and bias values and also activation function (i.e sigmoid, ReLU, GeLU). There is a software used to open that pre-trained model called Netron by the way.

Is there any range values occured for weight and bias ? For example from -1 to 1 like that?

Thanks for this. However can I ask, How do plot those weights and identify the input with the highest weight

Beautifully explained, thank you very much.

Thank you very much sir, very nicely explained

Very nice sir but one thing I want to know ,on which base the weights are given? Or it is just supposition?

Beautifully Explained . Couple of quick questions 1) How do you decide on the number of neurons in a hidden layer ? 2) How do you decide on the number of hidden layers ? . Thanks

Finally I watched the right lecture.

Very bad, what is weight what is the purpose of it, what is bias what is the purpose of it, that is why I watch this video, you don't explain any of them

Hi. Thanks for the video. Can each input be linked to only a subset of nodes in the hidden layer?
Thank you.

Thank you for this explanation. Concepts with examples really helps in easy learning

Great session.. In my opinion Using big board is better na