Choosing Between Map-style and Iterable Datasets in PyTorch
Learn when to use Map-style vs. Iterable datasets in PyTorch to avoid memory OOMs and I/O bottlenecks, including a guide on preventing data duplication in multi-process loading.
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pytorchOptimizing Deep Learning Workloads with Kaggle GPU Accelerators
Learn how to correctly enable and implement GPU acceleration in Kaggle Notebooks, including PyTorch device mapping and managing VRAM quotas.
kaggleStopping the Slide: Preventing Overfitting with Keras EarlyStopping
Stop wasting compute and prevent overfitting in Keras. Learn how to use the EarlyStopping callback to automatically terminate training at the peak of model performance.
kerasManaging Dynamic Computational Graphs with PyTorch Autograd
Learn how PyTorch's define-by-run architecture enables dynamic computational graphs, allowing for flexible model logic and variable-length inputs while managing VRAM efficiently.