Prompt: Tinygrad, developed by George Hotz (geohot), is a streamlined autograd library inspired by Micrograd. It aims to provide a simple and powerful solution for implementing deep learning models. Tinygrad is designed to be hardware-agnostic and supports both inference and training. It offers a similar API to PyTorch but with a simpler and more refined approach. Despite its simplicity, Tinygrad supports full forward and backward passes with autodiff, making it suitable for various machine learning projects .
Prompt: Micrograd, created by Andrej Karpathy, is an ultra-minimal autograd library with only 100 lines of Python code. It aims to demonstrate the inner workings of deep learning with simplicity and elegance. Micrograd decorates functions and traces operations to compute gradients, providing a foundation for understanding autograd and its role in deep learning.
Prompt: The category of autograd libraries that includes Tinygrad and Micrograd is focused on providing a simple and minimalistic implementation of automatic differentiation (autograd) for deep learning models. Autograd is a technique that allows the computation of gradients automatically, which is crucial for training neural networks using techniques like backpropagation.
Prompt: If Leonardo da Vinci would have drawn Mona Lisa as an artsy hippie boho with rainbow coloured hair in mixed style of surrealism and romanticism
Dream Level: is increased each time when you "Go Deeper" into the dream. Each new level is harder to achieve and
takes more iterations than the one before.
Rare Deep Dream: is any dream which went deeper than level 6.
Deep Dream
You cannot go deeper into someone else's dream. You must create your own.
Deep Dream
Currently going deeper is available only for Deep Dreams.