Popular GitHub Repositories for Mulit Mnist

Collection of generative models in Tensorflow

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

Collection of generative models in Pytorch version.

GPU Accelerated t-SNE for CUDA with Python bindings

A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".

[ACL 2024 ?] Video-ChatGPT is a video conversation model capable of generating meaningful conversation about videos. It combines the capabilities of LLMs with a pretrained visual encoder adapted for spatiotemporal video representation. We also introduce a rigorous 'Quantitative Evaluation Benchmarking' for video-based conversational models.

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification

Pure python implementation of SNN

Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.

AoE (AI on Edge，终端智能，边缘计算) 是一个终端侧AI集成运行时环境 (IRE)，帮助开发者提升效率。

Experiments for understanding disentanglement in VAE latent representations

A single handwritten digit classifier, using the MNIST dataset. Pure Numpy.

Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.

A free audio dataset of spoken digits. An audio version of MNIST.

TensorFlow2教程 TensorFlow 2.0 Tutorial 入门教程实战案例

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets

Implementation of CNN on ZYNQ FPGA to classify handwritten numbers using MNIST database

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset