Abstract: This paper investigates the application of Convolutional Neural Networks (CNNs) in MNIST handwritten digit recognition, with a particular focus on optimizing the ResNet-18 model. By ...

近年来，由于诸如自动编码器等深度神经网络（DNN）的高表示能力，深度聚类方法发展迅速。其核心思想是表示学习和聚类可以相互促进：好的表示会带来好的聚类效果，而好的聚类为表示学习提供良好的监督信号。关键问题包括：1）如何优化表示学习和聚类？

Abstract: The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems. Contributing to its widespread adoption are the understandable and intuitive ...

降维是在我们处理包含过多特征数据的大型数据集时使用的，提高计算速度，减少模型大小，并以更好的方式将巨大的数据集可视化。这种方法的目的是保留最重要的数据，同时删除大部分的特征数据。 在这个教程中，我们将简要地学习如何用Python中的稀疏和 ...

"The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The ...

Deep learning, which is basically neural network machine learning with multiple hidden layers, is all the rage—both for problems that justify the complexity and high computational cost of deep ...

Google's open source framework for machine learning and neural networks is fast and flexible, rich in models, and easy to run on CPUs or GPUs What makes Google Google? Arguably it is machine ...

"In the [MNIST tutorial](https://github.com/caffe2/caffe2/blob/master/caffe2/python/tutorials/MNIST.ipynb) we use an lmdb database. You can also use leveldb or even ...