• β€œTo be exact, after reading this book you will know:
    • How to train models that achieve state of the art results in:
      • Computer vision: Image classification (e.g. classify pet photos by breed), and image localization and detection (e.g. find where the animals in an image are)
      • Natural Language Processing (NLP): Document classification (e.g. movie review sentiment analysis), and language modelling
      • Tabular data (e.g. sales prediction) with categorical data, continuous data, and mixed data, including time series
      • Collaborative filtering (e.g. movie recommendation)
  • ”<β€” from: Howard and Gugger: Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD (book website includes GPU options and other Jupyter administrivia) <β€” sounds too good to be true :-) but the jupyter notebooks sound great

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