This CNN-based model for recognition of hand written digits attains a validation accuracy of 99.2% after training for 12 epochs. Its trained on the MNIST dataset on Kaggle.

驭风计划，深度学习: 1. Softmax实现手写数字识别(MNIST)； 2. 多层感知机实现手写数字识别(MNIST)； 3. PyTorch实战: CIFAR图像分类(CIFAR10)； 4. 脑部MRI图像分割(Kaggle-Brain MRI)； 5. 滴滴出行-交通场景目标检测(MMDetection,MMCV,COCO)； 6. 图像自然语言描述生成(COCO2014)； 7. SRGAN图像超分辨(DIV2K)。

CNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995).

A complete solution for the MNIST handwritten digit classification challenge using PyTorch, including data exploration, model training, and Kaggle submission generation.

Used the Dataset "MNIST Digit Recognizer" on Kaggle. Trained Convolutional Neural Networks on 42000 Training Images and predicted labels on 28000 Test Images with an Validation Accuracy of 99.52% and 99.66% on Kaggle Leaderboard.

# MNIST dataset downloaded from Kaggle : #https://www.kaggle.com/c/digit-recognizer/data

一步步带你通过项目（MNIST手写识别）学习入门TensorFlow以及神经网络的知识

Simple ConvNet to classify digits from the famous MNIST dataset

The project provides a step by step guide to solving and winning the MNIST competition on Kaggle. MNIST is a famous computer vision dataset that is often cited as a "Hello World!" for Machine Learning

Classifying MNIST dataset usng CNN (for Kaggle competition)

This project uses a retrained TensorFlow Inception model to make predictions for the Kaggle MNIST competition. Results are then compared to those from a handmade CNN.

Code for https://www.kaggle.com/competitions/ultra-mnist

Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images

This is the code I created to find solutions to the Kaggle MNIST digit recognition competition using a Convolutional Neural Network.

This repository contains Python code for handwritten recognition using OpenCV, Keras, TensorFlow, and the ResNet architecture. The project utilizes two datasets: the standard MNIST 0-9 dataset and the Kaggle A-Z dataset. The OCR model is trained using Keras and TensorFlow, while OpenCV is used for image pre-processing.

Building conv neural nets using theano and lasagne.

Train a CNN on mnist using PyTorch for kaggle submission test

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Digit Recognizer Kaggle challenge using the famous MNIST dataset.

This is a project that showcases my knowledge with Neural Networks. For me to illustrate this I will be only using the Numpy package in python and no machine learning packages. The neural network will have an adjustable number of layers and nodes. The activation function per layer can also be changed from either relu, sigmoid or tanh. I will be using a the MNIST dataset and predict whether the image is the number one (1) or it is a different digit (0,2,3,4,5,6,7,8,9) in which it will be labeled as zero (0). I got the MNIST dataset from kaggle so, it might no longer be the original.

The MNIST DIGIT RECOGNIZER COMPETITION ON KAGGLE. The training dataset consists of 42000 rows each of 784 pixel values thus representing 28 x 28 sized 42000 images of different digits from 0 to 9 . I have trained Convolutional Neural Networks written in Keras to train the model and predicted on the 28000 images of the test dataset, Also achieved 99.43% accuracy on Kaggle with 20 epochs . Also used ImageDataGenerator to augment the training set and avoid overfitting problem .

For my first contest in Kaggle, I have adopted an existing kernel submission for MNIST dataset digit prediction and toggled with the parameters of the CNN. This is my first experience of using Keras with Tensorflow backend. I have used knowledge from the "Deep Learning Specialization" course by Andrew Ng to design the CNN.

[Done] [Top 7%] A tensorflow based standard NN for Kaggle mnist competition (score 0.995)

Classify handwritten digits using machine learning techniques Yan Liang, Yunzhi Wang and Delong Zhao Project scope For our machine learning project, we propose to build several machine learning classifiers that recognize handwritten digits. Handwritten digit recognition is a classic problem in machine learning studies for many years. We plan to do several experiments using different machine learning algorithms and compare the pattern recognition performance. We hope to create a classifier that has same or better categorization accuracy than record performance from previous studies. Yan will focus on neural network, Delong will focus on the random forests methods, and Yunzhi will focus on SVMs and KNNs. We will also develop a final novel classifier that combines the best models from our different experiments. We hypothesize that the final classifier will archive a categorization accuracy of 0.99. This indicates that the classifier correctly classified all the handwritten digits but 1% of the images. The goal of handwritten digit recognition is to determine what digit is from an image of a single handwritten digit. It can be used to test pattern recognition theories and machine learning algorithms. Preprocessed standard handwritten digit image database has been developed to compare different digit recognizers. In our semester project, we will use modified National Institute of Standards and Technology (MNIST) handwritten digit images dataset from kaggle digit recognizer project. The Kaggle MNIST dataset is freely available and collected 28,000 training images and 42,000 test images. Each image is a preprocessed single black and white digit image with 28 x 28 pixels. Each pixel is an integer value range from 0 to 255 which represent the brightness of the pixel, the higher value meaning darker. Each image also has a label which is the correct digit for the handwritten image. For each input handwritten image, our model will output which digit we predict and evaluate with the correct label. We will use 28,000 training images to train our machine learning model and use 42,000 test images to test the performance. Then we will calculate the percentage of the test images that are correctly classified and compare the performance of different machine learning algorithms.

一种美式手语字母识别系统的设计与实现，通过卷积神经网络（CNN）训练不同手势对应的24个英文字母（不包括J和Z），可将结果实时显示在画面上。本项目数据集来自Kaggle平台的“Sign Language MNIST”数据集，每张图片的尺寸都为28*28像素，包含24类英文字母（不包含J和Z）。

Result = 97.2799, Submission for Kaggle mnist Challenge with Simple Neural Network

My kaggle submission for MNIST dataset

This repository consists of the Analysis and ML training of the MNIST_Digit_recognizer on kaggle

Kaggle Top 4% Project. CNN Based high precise MNIST like Kannada digit recognizer

Kaggle Digit Recogniser contest, Algorithms used:- K-nearest neighbour, Multi-layer perceptron, Dataset:- MNIST