Welcome to the Fashion MNIST Classification Workshop! In this session, you'll learn how to build, train, and evaluate a neural network that can accurately classify clothing items from the Fashion MNIST dataset. This workshop is designed for beginners and intermediate learners interested in deep learning and image classification.

Before we dive in, make sure you have the following prerequisites ready:

• Python 3.x installed
• Familiarity with basic Python programming
• An installed version of TensorFlow and Keras
• Jupyter Notebook or any Python IDE of your choice

To set up your environment for the workshop, follow these steps:

1. Clone the workshop repository: https://github.com/maajidhusain/HooHacks-Presentation.git

2. Install the required Python packages:

All workshop material is provided in the `Fashion_MNIST.ipynb

**For more experienced programmers there is another option which I have yet to improve located in the Diff_Proj_for_experts folder

The workshop is divided into the following sections:

1. Introduction to TensorFlow and the Fashion MNIST dataset
2. Loading and Preprocessing the Data
3. Model Architecture

• Building a simple 3-layer neural network

4. Model Compilation

• Configuring the model for training

5. Model Training

• Training the model and validating its accuracy

6. Model Evaluation

• Assessing the model's performance on the test set

7. Experimentation

• Encouraging participants to tweak the model to improve performance

Each section is accompanied by a Jupyter Notebook cell or markdown explanation, guiding you through the concepts and practical implementations.

We welcome contributions from participants and the community. If you have suggestions to improve the workshop, please fork the repository and submit a pull request.

This workshop is provided under the MIT License. See the LICENSE file for more details.

• Thanks to the TensorFlow and Keras teams for making such powerful tools accessible.
• Shoutout to all the participants who make these workshops engaging and fun.
• Medium Article

Happy learning, and we can't wait to see what you'll build!