codesnips

deep-learning

python 
import torch

device = 'cuda' if torch.cuda.is_available() else 'cpu'
features = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True, device=device)
weights = torch.tensor([[0.2], [0.8]], requires_grad=True, device=device)

PyTorch tensor basics and automatic differentiation

pytorch tensors autograd
by Dr. Elena Vasquez 1 tab
python 
import torch.nn as nn
from torchvision.models import resnet50, ResNet50_Weights

model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)

for parameter in model.parameters():

Transfer learning with pretrained torchvision backbones

pytorch transfer-learning torchvision
by Dr. Elena Vasquez 1 tab