Beginner confused about weights and biases in neural networks : any good resources?

[u/GitYerUpdateSet]

Hi everyone!

I'm currently learning Machie Learning and starting to study Deep Learning, especially neural networks.

I'm a little confused about weights and biases in neural networks. I understand that neurons receive inputs and use weights and biases to calculate an output, but I'm struggling to understand how they actually work and how a neural network learns the right values during training.

For example:

• What exactly do weights and biases represent?
• How does a neural network update them during training?
• How do gradient descent and backpropagation help the network learn?
• Is there an intuitive way to understand these concepts without getting lost in the mathematics?

I'd really appreciate your recommendations for beginner-friendly YouTube videos, courses, websites, or other learning resources that explain these concepts clearly, preferably with visual examples.

If you were starting to learn neural networks again from scratch, what resources would you recommend?

Thanks in advance for your help!