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Wow, this is cool — thank you for building and sharing it! I can’t wait to have one of these on my desk :)

Thank you, I appreciate it!

Thanks so much - glad to hear the Jim part, especially since it drove me nuts when I started generating text.

I wrote a paragraph for the FAQ on the over-fitting on Jim's dad monologues - not sure if it's true, but I think it's fun to ponder:

> For some reason the model picked on the monologues of Jim's (James Incandenza’s) dad. I found it funny that many sentences in some of the rambling monologues that the model is spewing end with ", Jim.". It's been a while since I last read the book, but I don't think these parts comprised more than, say, 5% of the book. It's interesting that the model fixated on them so much. My pet theory is that the model sees a rambling semi-coherent monologue it's producing, and then looks back at it and thinks "Oh, I must be inside one of these dumb Jim's dad monologues"

Glad you like it! :)

In the beginning I wanted to make it as close to being an Infinite Jest fanfic as possible. In hindsight - adding more of DFW works wouldn't have hurt. I might get round to it.

The main page displays prose generated by a state-of-the-art machine learning model (Open AI's GPT-2) fine-tuned on Infinite Jest, a novel by David Foster Wallace.

Part of each sequence of several paragraphs generated by the model is fed back into it as the prompt for the next original chunk of machine-generated prose.

The process repeats. The result is Entertainment.

Thanks for sharing these guides. Excited about exploring tf.keras with Eager Execution! :)

Which version were you installing? Version 0.4 released in April added Windows support.

These are great points, thanks.

Glad you like the article and thanks for suggesting researching the CPU usage across these frameworks - it's something worth looking into.

Thanks, appreciate it!

Author here - the article compares Keras and PyTorch as the first Deep Learning framework to learn. It explores the differences between the two in terms of ease of use, flexibility, debugging experience, popularity, and performance, among others.

If you have experience with learning, or teaching Deep Learning with PyTorch or Keras, we’d love to hear your thoughts about them.