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Comparative-Models for MNIST Dataset

Home Page: https://www.kaggle.com/justabhishekprasad/comparitive-models-for-digit-recognizer

Jupyter Notebook 100.00%
machine-learning mnist-dataset comparitive-study python vgg16 vgg autoencoder-mnist cnn-model confusion-matrix

mnist-digit-recognizer's Introduction

MNIST-Digit-Recognizer

MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.

In this competition, your goal is to correctly identify digits from a dataset of tens of thousands of handwritten images. We’ve curated a set of tutorial-style kernels which cover everything from regression to neural networks. We encourage you to experiment with different algorithms to learn first-hand what works well and how techniques compare.

Code Description

The ipynb File consist of all the variations of CNN
1)Simple RNN
2)AutoEncoder RNN
3)VGG-16 CNN
4)VGG-19 CNN

To find out more about the competitions here!

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