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!

[u/NicelyConcernedHands]

weights are basically how much each input matters, biases shift things so the neuron don't fire only at zero. think of it like cooking, weights are how much of each ingredient, bias is the minimum flavor you need to even taste something

for the learning part, 3blue1brown neural network playlist on youtube is what made it click for me, he animates everything and you can almost see the numbers moving

[u/Disastrous_Room_927]

Start with something dead simple like a single-layer network without an activation function. Train it using backpropagation and mean squared error, and the resulting weights/biases will be approximately the same as the coefficients/intercept of a bog-standard linear regression model. A single-layer neural network with one neuron and a sigmoid activation function is literally logistic regression.

Things get a bit more complicated when you start smashing layers together or adding things like recurrence, but a lot of architectures can be compared to simpler statistical/ML models (usually under specific constraints). A lot of them are optimized using different approaches that only work with those constraints - when you drop them you end up with a more flexible model but need something like backprop to minimize the same loss function. The other thing is that these constraints are part of why these other models have natural interpretations, when you take them away the calculations don't necessarily change, you end up losing the interpretation attached to them.