junaidfiaz143/Shallow-MNIST-with-TF-and-KERAS
This is the code for "predict MNIST datasets using pure Tensorflow and Keras, a shallow learning model" By M.Junaid Fiaz
MNIST MultiClass Classification
In this project you will discover how to effectively pure Tensorflow to predict the numbers in MNIST dataset as well as in high-level ML api Keras.
Getting Started
Description of the Dataset
Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images.
You can learn more about this dataset on the Keras website:
https://keras.io/datasets/
What we achieved doing this Project:
In this post, you discovered the MultiClass classification using TF as well as in Keras Deep Learning library in Python.
You learned how you can work through a binary classification problem step-by-step with Keras, specifically:
- How to load and prepare data for use in TF/Keras.
- How to create a neural network model.
- How data preparation schemes can lift the performance of your models.
- How experiments adjusting the network topology can lift model performance.
Prerequisites
What things you need to install the software and how to install them
puthon IDE
jupyter notebook
Built With
- python - Programming Language
- tensorflow - TensorFlow is an open-source machine learning library for research and production
- keras - Keras is a high-level neural networks API
- numpy - NumPy is the fundamental package for scientific computing
Authors
- M.Junaid Fiaz - JD
License
This project is licensed under the APACHE License - see the LICENSE.md file for details