Knowledge Transfer

Access an individual batch from the DataLoader, we first pass the DataLoader object to Python’s iter() built-in function, which returns an object representing a stream of data.

Computing the binary loss by providing the logits, and not the class probabilities is usually preferred due to numerical stability.

you could just sum it and calculate the mean after the epoch finishes or at the end of the epoch, we divide by the number of steps(dataset size). It gives you the correct average sample loss for this particular epoch. This training loss is used to see, “how well your model performs on the training dataset”.

The workaround is to use log probability instead of probability, which takes care to make the calculation numerically stable. The reformulated version allows us to evaluate softmax with only small numerical errors even when z contains extremely large or extremely negative numbers.

Contiguous tensors are convenient because we can visit them efficiently in order without jumping around in the storage. It improves data locality and improves performance because of the way memory access works on modern CPUs. This advantage of course depends on the way algorithms visit.

The saturated neurons can kill gradients if we’re too positive or too negative of an input. They’re also not zero-centered and so we get these, this inefficient kind of gradient update. The third problem is an exponential function. This is a little bit computationally expensive.

You can evaluate the relationship between each feature and target using a correlation and selecting those features that have the strongest relationship with the target variable. Such as methods that remove redundant variables using correlation.

You can keep adding plt.plot as many times as you like. As for multiple scatter plot, you need to specify the color, marker, edgecolor, alpha so you can differentiate them.

We’ll look at a real-world example with data that I’ll load from a CSV file and also we’re going to learn how to draw overlapping histograms using Pandas and Matplotlib. Before we go and set up how we create the chart let’s look at what the end results would be.

we want to make a copy of a tensor and ensure that any operations are done with the cloned tensor to ensure that the gradients are propagated to the original tensor, we must use clone(). We should use detach() when we don’t want to include a tensor in the resulting computational graph.