• Mar 15, 2026 pytorch deep learning hands on build cnns rnns ga h import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms Define the CNN Architecture class SimpleCNN(nn.Module): def __init__(self): super(SimpleCNN, self).__init__() self.conv1 = nn.Conv2 By Nadine Leannon
• Apr 13, 2026 pytorch an introduction guide to pytorch deep lea s, exploring advanced concepts enhances your deep learning pipeline. Transfer Learning and Pretrained Models Use models pretrained on large datasets like ImageNet. Fine-tune for specific tasks, saving time and resources. Custom Layer By Modesta Nicolas
• Jan 5, 2026 natural language processing with pytorch build in _size=25000, vectors='glove.6B.100d') LABEL.build_vocab(train_data) Create iterators train_iterator, test_iterator = data.BucketIterator.splits( (train_data, test_data), batch_size=64, device=torch.device('cuda') ) Define model class TextClassifier(nn.Module): def __init__(self, voca By Mr. Edward Walter
• Mar 28, 2026 natural language processing mit pytorch intellige version of LSTMs with similar performance. Transformers: State-of-the-art models like BERT and GPT that rely on attention mechanisms to process sequences more efficiently. Attention Mechanisms and Transformers A By Mr. Austin Walsh
• Aug 8, 2025 deep learning with pytorch Torch Before diving into code, it’s critical to understand the core components that underpin deep learning workflows in PyTorch. Tensors: The Building Blocks Tensors are multi-dimensional arrays that serve as the primary data structure in PyTorch. They are similar to NumPy arrays bu By Freda Abshire
