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.
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/
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.
What things you need to install the software and how to install them
puthon IDE
jupyter notebook
- 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
- M.Junaid Fiaz - JD
This project is licensed under the APACHE License - see the LICENSE.md file for details