Jeremy Howard (

Jeremy Howard is the co-founder of, a research institute dedicated to making deep learning more accessible. Previously, Jeremy founded a med tech startup Enlitic, and was President of Kaggle.

  • Instead of automating the machine learning process, we should study how to augment it via human-in-the-loop.

  • is a unique visual and code-free tool that labels images and trains computer vision models.

  • Here are lessons learned from optimizing hyper-parameters for image datasets using

    • Stick with a sensible learning rate (most of the time, the default is good).

    • With Test-Time Augmentation search, you can beat state-of-the-art results even if they use specialized models.

    • Progressive resizing is amazing.

    • Heatmaps are useful to visualize what's happening.

    • 1cycle is a big time-saver.

    • For transfer learning, always train later layers more: (1) gradual unfreezing and (2) discriminative learning rates.

    • Use AdamW optimizer.

    • If you are doing tons of epochs, consider clipping gradients or annealing Adam's episodes.

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