Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

Tools for (approximately) visualizing the architures of existing neural networks or for visualizing the traing process (training/validation loss/accuracy, activation, etc.) are extremely helpful! TensorBoard is one of the best …

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

1. Cross validation in scikit-learn supports pipeline in addition to vanilla models. Please refer to Cross Validation Pipeline for more details.

2. Label encoding is an easy way to convert a categorical response …

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

GPU Related Issues and Solutions

Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

This means that the input data and the model are on different …

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

1. torchvision pretrained models

2. Model Zoo

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

Memory

1. ResNet152 inferencing requies about (a little bit less than) 2G GPU memory and training can be done with 8G GPU memory (need to set a small batch size). Deeper ResNet …

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

https://pypi.org/project/lightgbm/#build-gpu-version

https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version

https://www.kaggle.com/vinhnguyen/gpu-acceleration-for-lightgbm

Microsoft's Example Dockerfile for GPU version of LightGBM …