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.

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

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

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.