Build Your First Pytorch Model In Minutes! [Tutorial + Code] 

I don't do computer science or university or anything, but I think that you're are an example for other educators.

😂😂 In my case if I mail my teacher, I highly doubt he would open the mail let alone try to teach like this channel.

I just asked ChatGPT and it seems like you have to turn on internet on Kaggle instances under notebook options in the right sidebar!

Check ouit my videos on yolov5 and yolov7. They are all about object detection.

hi @@robmulla, I meant using torch. I couldnt find any source that implementing object detection model using pytorch, all the object detection videos made by prepared models such as yolov5, yolov8, ssd, vgg etc.

In an earlier line we gave the Adam optimized the model.parameters(). When we do all our computations through our model and in the end through our loss these computations will be stored in an internal state of the model. When we then call loss.backward() all these steps our tensors took through the model (and the loss) are run in reverse order and the backwards calculated gradients are also stored in the internal state of our model. And now hence the Adam optimized has the connection to the model parameters and we stored the backwards calculated gradients in our hidden state in the model, it is enough to call optimizer.step() and let the Adam do his magic. I hope this helps.

@@felixmuller9062 Wait that's the part that I don't get: the loss is computed based on the model predictions, but the loss object doesnt 'know' about the parameters right? Like, the model parameters are not an argument of the loss creator function

No but the loss "measures" the deviation from the true classification. When you look in the formulas of the backpropagation algorithm you will see that the first derivative is the one regarding the loss function. From there on, following the chain rule, this flows in the different layers of the network and that's how the gradients for the weights can be calculated. Does this clarify a little more?

@@felixmuller9062 Ah I see, thanks! so just to make sure I get it: 'loss' is given a reference to the parameters of the model, and this parameters object has a 'bin' for gradients, so loss.backward() populates this bin? I think I was thinking too functional---the key is that backward does have a side effect on an object outside of it's 'house'. (Like loss.backward() modifies not only the loss object but also the parameters object.)

@@felixmuller9062 Wait nevermind, I'm confused again: the loss function does not get information about the model parameters or their location in memory. It just receives the predicted outputs and the correct labels. How does it know where to store the gradients computed?

Nvidia really is the only option.

i also have this problem, did you solved it?

ok solved it, you just have to turn on the internet on the kaggle notebook

@@marco8673 tnx

LocalEntryNotFoundError: Connection error, and we cannot find the requested files in the disk cache. Please try again or make sure your Internet connection is on.