Richard – A CNN written in C++ and Vulkan (no ML or math libs)

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This started out as a personal effort to learn more about machine learning. It's currently a CLI app where you give it a JSON file specifying your network architecture and hyperparameters and point it to your training data, then invoke it again in 'eval' mode with some data it's not seen before and it will try to classify each sample. I don't see many other people using Vulkan for GPGPU, and there may be many good reasons for that, but I wanted to try something a bit different. I've made every attempt to make the code very clean and readable and I've written up the math in documentation/math.pdf, so hopefully this is a useful learning resource for anyone interested in how neural nets work under the hood. I'll be continuing to add new features over the coming months.

Visit product View on Hacker NewsMar 15, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, using · Missing: agents, macos, agent

69%69% 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.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: personal, month, para · Missing: mobile apps, ios, entrepreneurs

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

Indie HackersIH features products with proven revenue · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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