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🖼️ Basic Image Classification with TensorFlow

This repository contains a Jupyter Notebook that demonstrates a basic image classification task using TensorFlow. The notebook provides a step-by-step guide to building, training, and evaluating a simple image classification model.

📄 Contents

  • Basic-Image-Classification-with-TensorFlow.ipynb: A Jupyter Notebook that includes all the necessary code and explanations for performing basic image classification using TensorFlow.

🚀 Getting Started

To get started with this project, follow the instructions below:

Prerequisites

Ensure you have the following installed:

  • Python 3.x
  • Jupyter Notebook
  • TensorFlow

You can install the required packages using pip:

pip install tensorflow jupyter

🏃 Running the Notebook

  1. Clone this repository to your local machine:
 git clone https://github.com/akhilaprabodha/handwritten-digit-classification
cd your-repo-name
  1. Launch Jupyter Notebook:
jupyter notebook
  1. Open Basic-Image-Classification-with-TensorFlow.ipynb in Jupyter Notebook and run the cells sequentially.

📚 Project Overview

The notebook covers the following steps:

  1. Importing Libraries: Load the necessary libraries such as TensorFlow and other dependencies.
  2. Loading Data: Download and prepare the dataset for training and testing.
  3. Building the Model: Define the architecture of the neural network using TensorFlow's Keras API.
  4. Compiling the Model: Specify the loss function, optimizer, and metrics for evaluation.
  5. Training the Model: Train the model on the training dataset.
  6. Evaluating the Model: Evaluate the model's performance on the test dataset.
  7. Making Predictions: Use the trained model to make predictions on new data.

🛠️ Example

Here's a brief example of what the notebook covers:

  • Loading and preprocessing the dataset.
  • Building a Convolutional Neural Network (CNN) model.
  • Training the model and visualizing training progress.
  • Evaluating the model on a test set.
  • Making predictions and visualizing the results.

💽 MNIST Dataset: A Foundation for Image Classification

The MNIST dataset serves as a cornerstone for image classification tasks. It's a widely recognized collection of handwritten digit images (0-9) that has become a standard benchmark for evaluating and comparing image recognition models. Here's what makes MNIST such a valuable resource:

  • Accessibility: MNIST is conveniently available within TensorFlow's keras.datasets module. This allows users to effortlessly load and utilize the data for training and evaluating their models.
  • Manageable Size: The dataset comprises a total of 70,000 images, further divided into 60,000 training and 10,000 testing images. This manageable size makes it a great starting point for beginners in image classification.
  • Straightforward Format: Each image in the MNIST dataset is a grayscale image with a resolution of 28x28 pixels. This simple format simplifies data preprocessing and allows models to focus on the core task of digit classification.
  • Clear Labels: Every image is accompanied by a corresponding label indicating the depicted digit (0-9). These clear labels facilitate supervised learning, where the model learns to map image features to their respective digit labels.
  • Benchmarking Ground: MNIST has been extensively used in the field of image recognition research and development. This extensive usage establishes a common ground for evaluating and comparing the performance of various models and algorithms.

Why Choose MNIST for Image Classification?

Given its numerous advantages, MNIST is an exceptional choice for projects exploring image classification:

  • Simplicity for Beginners: The relatively small size and straightforward nature of the MNIST dataset make it ideal for those new to image classification. It offers a manageable learning environment to grasp the fundamentals of training and evaluating image recognition models.
  • Well-Defined Task: The task of classifying handwritten digits is well-understood and provides a clear objective for model development. This focused objective allows learners to concentrate on building and refining their models without getting overwhelmed by complex classification problems.
  • Established Benchmark: The extensive use of MNIST in research creates a well-established benchmark. This allows users to compare their models' performance against existing results and track their progress in image classification tasks. By leveraging the ease of use, clear task definition, and established benchmark nature of the MNIST dataset, your image classification project gains a solid foundation for exploration and learning.

🤝 Contributing

Contributions are welcome! If you have any suggestions or improvements, please open an issue or submit a pull request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

  • TensorFlow documentation and tutorials
  • The open-source community for providing valuable resources

📖 Resources:

Additional:

Check this out Neural Network Rectified Linear Unit (ReLU) vs Sigmoid