This is a Neural Network class I created in Python along with some test data.
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Updated
Feb 14, 2017 - Python
This is a Neural Network class I created in Python along with some test data.
Just another BP simulation.
Using Tensorflow LSTM model to train and recognize handwritting digits from Kaggle handwritting digit competition.
Machine Learning algorithm using ADAM regression for recognizing handwritten numbers
Implementation of CNNs for handwritten digit recognition
Tensorflow KR MNIST Competition & Kaggle MNIST Challenge
Problems Identification: This project involves the implementation of efficient and effective KNN classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.
Neural network/Back Propagation implemented from scratch for MNIST.从零开始实现神经网络和反向传播算法,识别MNIST
This is a repository with various classifier written in jupyter notebook that works on hand written digits images.
RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.
🎓 🐍 🌐 ✏️ A web application that accepts a file with handwriting and uses AI to recognize the writing. Developed in Python with TensorFlow and Flask on the back end and AngularJS with Bootstrap on the front end.
This is v1.2 of the Image Recognition Model for MNIST Dataset
This project involves the implementation of efficient and effective LinearSVC on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.
This project uses a neural network to classify hand written digits
3-layer feed-forward neural network using back-propagation to recognize MNIST digits
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