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I used to use Gmail until 4 months ago. I was really happy with this mail, it is easy to handle and, being a Google member, there are many tools available to use. However, I started to learn about the security and privacy offered by Google, which is NONE. We are selling our information and personal data to a technological giant and, many times, we are not even aware of it.
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I built the first version of DeepFork to understand micrograd โ Andrej Karpathy's 100-line autograd engine. Most people read micrograd for the aha moment. DeepFork turns that moment into an artifact. - Source: dev.to / about 2 months ago
Karpathy built a small project called micrograd. You can see the code here. This is made up of just a few simple lines of code, but it shows us how neural networks are built under the hood. In the video, he demonstrated how to build Micrograd and how it works step by step. - Source: dev.to / 2 months ago
It can happen like this: - write sleek operator-overloading-based code for simple mathematical operations on your custom pet algebra - decide that you want to turn it into an autograd library [0] - realise that you now need either `RefCell` for interior mutability, or arenas to save the computation graph and local gradients - realise that `RefCell` puts borrow checks on the runtime path and can panic if you get... - Source: Hacker News / 3 months ago
Good introduction! Building pytorch-lite using python and numpy is the way to go. Free book: https://zekcrates.quarto.pub/deep-learning-library/ Ml by hand : https://github.com/workofart/ml-by-hand Micrograd: https://github.com/karpathy/micrograd. - Source: Hacker News / 6 months ago
Let me be completely honest: I didn't invent anything here. This is Andrej Karpathy's brilliant micrograd ported to C++, nothing more, nothing less. But sometimes the best way to really understand something is to rebuild it in a different language, and that's exactly what I needed. - Source: dev.to / 11 months ago
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