Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Modules can hold parameters of different types on different devices, so it's not always possible to unambiguously determine the device. The recommended workflow in PyTorch is to create the device object …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
In statistics, a unit root test tests whether a time series variable is non-stationary using an autoregressive model. A well-known test that is valid in large samples is the augmented Dickey …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Complete Randomized Design (CRD)
Randomized Complete Block Design (CBD)
Balanced Incomplete Block Design
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Random Sampling
Systematic Sampling
Stratified Sampling
Convenience Sampling
Judgement Sampling
Quota Sampling
Snowball Sampling
bias
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
TabNine uses deep learning to help you write code faster. In paticular, jupyter-tabnine and TabNine VSCode are TabNine plugins for Jupyter Notebook (JupyterLab not support yet) and Visual Studio Code …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
The PyTorch doc Reproducibility has very detailed instructions on how to make your model training reproducible. Basically, you need the following code.
torch.manual_seed(args.seed)
np.random.seed(args.seed …