Tips and Traps¶

1. It is never a good idea to do pre-mature optimization. Always profile (please refer to Profile Performance of Python Applications and Python Profiler for JupyterLab Notebooks) your code before you optimize it. If you do decide to optimize you Python code, there are multiple approaches. First, think about whether you can improve your algorithm. Please refer to Python Performance Tips

Tips and Traps¶

1. The broadcast concept in numpy is essentially a way to "virtually" duplicate data in a numpy array so that it is "virtually" reshaped to be compatible with another numpy array for a certain operation. Do not confused yourself about it with the broadcast concept in Spark which sends a full copy of a (small) DataFrame to each work node for BroadCastJoin

Tips and Traps¶

There are multiple Kotlin kernels for JupyterLab. It is is suggested that you use official one released by JetBrains.

Jar Dependencies¶

You can add a JAR dependency using the following code.

Tips and Traps¶

1. Adpative pooling an generate fixed-sized output, which enables a model to handle varying sized input. The models in torchvision are such examples.

AdaptiveMaxPool2d¶

References¶

Torchvision ResNet Input Size