In some code for machine learning with transformers, I've seen the terms Batch, Time, Channels to describe dimensions of data. Can you explain what these are?
Sure! These terms are commonly used in deep learning and describe the dimensions of the data tensors.

Batch refers to the number of samples or instances in a single forward/backward pass of the neural network. In other words, it's the number of samples processed together at a time.

Time refers to the sequence length or the number of time steps in a sequence. For example, in natural language processing, this could refer to the number of words in a sentence.

Channels refers to the number of features in the data, such as color channels (R, G, B) in an image.

So, for example, if you have 100 images of size 32x32x3 (height x width x color channels), your data tensor might have the shape (100, 32, 32, 3), where the first dimension represents the batch size and the last three dimensions represent height, width, and color channels.
Oo, that was clarifying, what about the terms like epoch, temperature e.t.c
Here are explanations of some commonly used terms in machine learning with transformers:

Epoch: An epoch is a complete iteration over the entire training data set. During an epoch, the model is trained on each example in the data set once, updating its parameters. The number of epochs is a hyperparameter that can be chosen based on the specific use case, with more epochs often leading to better results but at the cost of longer training times.

Temperature: In the context of machine learning with transformers, temperature is a scalar value used to control the randomness of the model's outputs. For example, when generating text with a transformer model, a higher temperature will result in more diverse and unexpected outputs, while a lower temperature will result in more predictable outputs.