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Learn how to use the Keras Functional API to build modular models that encapsulate preprocessing and data validation, reducing training-serving skew and improving architectural flexibility.
Learn how to implement PyTorch DistributedDataParallel (DDP) to scale training across multiple GPUs using All-Reduce, gradient bucketing, and DistributedSampler.
Learn how to use torch.compile to eliminate Python overhead and fuse operators in PyTorch. This guide covers compilation modes, debugging graph breaks, and performance trade-offs.
Choose the right accelerator tier for deep learning on Kaggle Notebooks, with quota limits, framework affinity, and validation steps.
Learn how to handle Kaggle's free GPU and TPU quotas by implementing resumable training loops and choosing the right accelerator for your framework.
Avoid GPU stalls by building a tf.data pipeline that prefetches and parallel‑maps data. Learn how to set up a CIFAR‑10 pipeline, tune prefetch buffers, and monitor GPU utilization to keep training fast and memory‑efficient.
Keras EarlyStopping halts training when validation loss plateaus, saving compute and preventing overfitting--if you configure patience and restore_best_weights correctly.
Learn how to pick the right GPU, avoid memory fragmentation, and keep your experiments reproducible in Google Colab. A step‑by‑step guide with code, trade‑offs, and practical checks.
Stop letting your CPU hold back your GPU. Learn how to use tf.data's prefetch, cache, and parallel mapping to eliminate GPU starvation and speed up training.
keras
pytorch
kaggle
tensorflow
googlecolab