Design a patches masked autoencoder by CNN

This task focuses on training the MINIST dataset to get a feel for the differences between fully connected networks, neural networks, and the effects of different activation functions, training techniques (BN, Dropout, and L2 regularization) on the network. 本次任务主要是通过对MINIST数据集进行训练来感受全连接网络、神经网络的差异，以及不同激活函数、训练技巧（BN，Dropout以及L2正则化）对网络的影响。

TensorFlow与Flask结合打造手写体数字识别 | 学习网站：

This repository demonstrates a basic image classification project using TensorFlow, focusing on recognizing handwritten digits.

MNIST-DS Simple training program

MINIST Image Digit Recognition by means of a MPL fully connected neural network

Machine Learning and Neural Network techniques to recognize handwritten digits with high accuracy

Discover the Handwritten Digit Recognition! 🚀 Unleash the power of neural networks with this code snippet, as it delves into the world of AI-driven Handwritten Digit Recognition. The celebrated MNIST dataset serves as the foundation, guiding the process of building, training, and evaluating models for precise digit predictions.