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Stop limiting your neural networks to linear stacks. Learn how to use the Keras Functional API to build multi-input and multi-output architectures for complex data.
Learn how to implement non-linear model topologies using the Keras Functional API, including multi-branch designs, data boundary enforcement, and operational verification.
A decision guide that compares the Functional API and subclassing styles in Keras, shows a concrete MNIST example, and explains how to validate equivalence and deployment readiness.
Learn how to switch Colab’s runtime to GPU, confirm it with nvidia-smi, and protect your work from session limits by checkpointing to Google Drive.
Validation loss often bottoms out mid-training. Pair EarlyStopping with ModelCheckpoint to keep the best epoch, match monitor modes correctly, and handle version-sensitive serialization.
Kaggle's free GPU accelerators come with weekly quotas and volatile sessions. Learn how to verify your accelerator in code, checkpoint around timeouts, and treat GPU hours as a scarce resource.
Learn how to decide between CPU and GPU runtime in Kaggle Notebooks, compare the options, see a ready‑to‑use implementation with device detection and OOM fallback, and verify your choice with simple checks.
Google Colab’s free GPU tier can surprise users with sudden CPU switches and session timeouts. This blog explains how to select, monitor, and upgrade hardware accelerators for resilient deep‑learning notebooks.
torch.compile can speed up PyTorch with one line, but only if you benchmark past warmup, check for graph breaks, and handle dynamic shapes. Here's how to verify it actually helps.
keras
googlecolab
tensorflow
kaggle
pytorch