Implementation of CNN to recognize hand written digits (MNIST) running for 10 epochs. Accuracy: 98.99%
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Updated
Dec 16, 2018 - Jupyter Notebook
Implementation of CNN to recognize hand written digits (MNIST) running for 10 epochs. Accuracy: 98.99%
A resource-conscious neural network implementation for MCUs
An Intuitive Desktop GUI Application For Recognizing Multiple Handwritten Digits Drawn At The Same Time. Trained On MNIST Dataset and Built With Python, OpenCV and TKinter
🎰Handwritten digit recognition application implemented by TensorFlow2 + Keras and Flask.
Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.
MNIST of Tibetan handwriting 国产手写藏文MNIST数据集(TibetanMNIST)的图像分类处理与各种好玩的脑洞~
RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.
AWS Fundamentals Specialization Coursera
2021 Spring (Statistical Methods and Machine Learning) 统计方法与机器学习
Wrote a neural network that uses fundamental DL algorithms to identify handwritten digits from MNIST dataset.
This repo includes my solutions to the Coursera course offered by AWS titled "AWS Computer Vision: Getting Started with GluonCV", in addition to more tutorials and in-depth handson labs. Please 🌟 the repo if you like it ☝️ Create an Issue or preferably a PR for any improvement. 🚀
本仓库包含了完整的深度学习应用开发流程,以经典的手写字符识别为例,基于LeNet网络构建。推理部分使用torch、onnxruntime以及openvino框架💖
MNIST is the de facto “hello world” dataset of computer vision. In this competition, our goal is to correctly identify digits from a dataset of handwritten images.
Running some AI examples on NVIDIA Jetson Nano
It is a Python GUI in which you can draw a digit and the ML Algorithm will recognize what digit it is. We have used Mnist dataset
a simple neural network
FPGA Implementation of Image Processing for MNIST Dataset Based on Convolutional Neural Network Algorithm (CNN)
Basic tensorflow examples with code
Virtual Pen + Recognition of handwritten digits
Training a Neural network in Unity 3D to recognize handwritten digits from the MNIST dataset
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