For an upcoming project, I am trying to build a neural network for classifying text from scratch, without the use of libraries. This requires an embedding layer, or a way to convert words to some vector representation. I understand the gist, but I can't find any deep explanations or tutorials that don't start with importing TensorFlow. All I'm really told is that it works by context using a few surrounding words, but I don't understand exactly how.
Is it much different from a classic network, with weights and biases? How does it figure out the loss?
If someone could point me towards a guide to how these things work exactly I would be very grateful.