Lambda functions are a great way to put small workloads into the cloud without needing to care about servers or scalability. They work well with Python and the data format you’ll most likely be using to exchange data is JSON. I help you listen through the noise in machine learning: This is a very brief post to show how to do this in Python, in particular how to pass JSON data to the lambda function and how to read the JSON result.

When you have a big data set and a complicated machine learning problem, chances are that training your model takes a couple of days even on a modern GPU. However, it is well-known that the cycle of having a new idea, implementing it and then verifying it should be as quick as possible. This is to ensure that you can efficiently test out new ideas. If you need to wait for a whole week for your training run, this becomes very inefficient.

Have you ever tried to plot a PyTorch tensor with matplotlib like: plt.plot(tensor) and then received the following error? AttributeError: 'Tensor' object has no attribute 'ndim' You can get around this easily by letting all PyTorch tensors know how to respond to ndim like this: torch.Tensor.ndim = property(lambda self: len(self.shape)) Basically, this uses the property decorator to create ndim as a property which reads its value as the length of self.

Recent advances in training deep neural networks have led to a whole bunch of impressive machine learning models which are able to tackle a very diverse range of tasks. When you are developing such a model, one of the notable downsides is that it is considered a “black-box” approach in the sense that your model learns from data you feed it, but you don’t really know what is going on inside the model.

An important consideration in machine learning is the shape of your data and your variables. You are often shifting and transforming data and then combining it. Thus, it is essential to know how to do this and what shortcuts are available. Let’s start with a tensor with a single dimension: import torch test = torch.tensor([1,2,3]) test.shape torch.Size([3]) Now assume we have built some machine learning model which takes batches of such single dimensional tensors as input and returns some output.