In this blog post, I will discuss how to use loss functions in PyTorch. I will cover how loss functions work in both regression and classification tasks, how to work with numpy arrays, the expected shape and type of loss functions in PyTorch, and demonstrate some types of losses.
In this blog post, I will discuss how to use loss functions in TensorFlow. I’ll focus on binary cross entropy loss.
In this post, I’ll demonstrate how to connect to Google Cloud Platform (GCP) instances using VSCode’s Remote SSH extention. We’ll assume you already have working GCP instances that you can ssh into. To connect to a remote host, VSCode needs to know the HostName, User, and IdentityKey. In this guide, we’ll go over how to find these. For simplicity, we’ll assume you’re trying to connect to an instance named my_instance and your zone is europe-west4-b. You’ll need to find these values and change them in the instructions below.
It’s important to know whether a model has been trained well or not. One way to do this is to look at the model weights. But it’s hard to know what exactly they’re telling you - you need something to compare the weights to. In this post, I’m going to look at the weight statistics for a couple of well-trained networks, which can be used as comparison points.
It’s often a good idea to construct deep learning code so that experiments can be conducted simply from the command line, allowing you to quickly iterate and record results. argparse makes this super easy by allowing configurations to be passed as command-line arguments. But sometimes you want to do the opposite. You find a repo that has a train.py file ready-to-go but you want to walk through it line-by-line in a Jupyter Notebook.
This post contains some of my notes on the argparse package.
In the research paper Group Normalization by Yuxin Wu and Kaiming He, they introduce the idea of group normalization. They show that it can be applied very easily by including the necessary code in the paper. This post walks through the code behind group normalization.
