DRUMPF-9000, a DeepDrumpf that can run in your browser

Hacker News

I was interested in testing how big of a recurrent net can be run in the browser, so I wrote some code to convert a Torch model to RecurrentJS. The answer is: you can run surprisingly big nets, as I was mostly limited by the size of the model itself (you wouldn't want users to wait for their download for too long). Blog post here: http://testuggine.ninja/blog/torch-conversion I demonstrated this by building a demo that is similar to @DeepDrumpf, but with the added requirement of running live in your browser. You can find it here: http://drumpf9000.com

Visit product View on Hacker NewsMar 15, 2016

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2points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, code · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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