#MNIST

This open-source project uses TensorFlow and Sonnet to enhance optimization with learning-to-learn strategies, focusing on command-line tools for efficient problem-solving, including MNIST and CIFAR10 classifications. It supports quadratic functions and employs advanced optimizers such as L2L. The flexible design allows easy integration of new problems and adjustment of parameters like learning rate and epochs, effectively showcasing TensorFlow's optimization capabilities across diverse challenges. Not officially affiliated with Google.

Discover a simplified implementation of scalable rectified flow models leveraging SD3 training techniques and LLaMA-DiT architecture. Ideal for beginners, this repository provides straightforward instructions for training on datasets such as MNIST and CIFAR, with further options for advanced exploration using ImageNet. Engage with rectified flow transformers easily, without requiring extensive prior knowledge, in a project that emphasizes accessibility and innovation in flow model methodologies.

Explore a practical approach to implementing machine learning models for handwritten digit recognition on Android using TensorFlow. This project provides a ready-to-use library via Maven, removing the necessity of manual setup. Understand how to train your own model through straightforward Python scripting, and discover ways to elevate Android development expertise. Participate in this open-source initiative by contributing and customizing it to meet varied specifications.