Deep Learning and Deep Integration via Jupyter
summary
A Jupyter-driven library called connects targets teaching deep learning at liberal-arts colleges by putting explanation notebooks into the design loop, not later. It ships as a pip-installable package, layers a simple neural-network dashboard on Keras/TensorFlow, and favors plain Python — using lists instead of NumPy for clarity. Visualizations render as SVG fragments that are updated independently in the notebook, and examples range from image segmentation and colorization to style transfer, generative knitting patterns, and references to AlphaZero-style self-play.
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chapters (6)
- 0:00Introduction and project goal
- 1:45Designing with Jupyter
- 5:16Redefining deep learning
- 9:10Deep learning applications
- 15:03Educational software philosophy
- 21:27Implementation and conclusion
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