https://github.com/syedsaadahmed/python_mnist_script
A small python project for learning and producing results from MNIST data set
https://github.com/syedsaadahmed/python_mnist_script
imageclassification keras-tensorflow machine-learning mnist-dataset python tensorflow
Last synced: 12 months ago
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A small python project for learning and producing results from MNIST data set
- Host: GitHub
- URL: https://github.com/syedsaadahmed/python_mnist_script
- Owner: syedsaadahmed
- Created: 2020-12-09T01:08:30.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-01-24T21:41:33.000Z (about 5 years ago)
- Last Synced: 2025-01-03T09:19:05.238Z (about 1 year ago)
- Topics: imageclassification, keras-tensorflow, machine-learning, mnist-dataset, python, tensorflow
- Language: Python
- Homepage:
- Size: 12.9 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PYTHON MNIST DATASET BASED PROJECT
A small python project for learning and producing results from MNIST data set. Developing a Convolutional Neural Network From Scratch for MNIST Handwritten Digit Classification.
# Pre-requisites
## Docker must be Installed in your system in order to execute it
Get it from here: https://docs.docker.com/get-docker/
# Execution
**First of all pull the Docker image from Dockerhub**
```
docker pull syedsaadahmed2094/machine_learning_image:v1
```
**Second, Move to the directory where you have clone the GIT repository.**
```
cd /path to git repo/
```
**Thirdly create the container from that Image, It contains all the dependencies for the script to execute**
```
docker run -itd -v $(pwd):/machine_learning_project [image_id] bash
```
**Get inside the container that is being created using the container ID**
```
docker attach [container ID]
```
**Finally you can find all the files of the repo inside the container, just go to**
```
cd /machine_learning_project
```
**Help taken for**
https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-from-scratch-for-mnist-handwritten-digit-classification/