CNN-for-Mnist-Digit-Datasets-Top-18-
Competition Description
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.
In this notebook I am using the MNIST Digits dataset. About the dataset: The dataset consists of 10 classes of handwritten Images pictures each with a number between 0-9.
In this notebook, I will go through the following items:
Data Understanding
Normalization
Display examples in the dataset
Preparing the inputs
Data augmentation
Adding the development set
Using Trasnfer Learning Architecture ResNet50, CNN Model
for more information about this kernel, check out my Kaggle Profile here:
https://www.kaggle.com/homayoonkhadivi/cnn-for-mnist-digit-datasets 12345678910111213456789111234567891121345678910111223456789111213