Build a CNN for Image Classification

Introduction:

This project focuses on building Convolutional Neural Networks (CNNs) for image classification, leveraging two distinct datasets: MNIST and Cats vs Dogs.

Dataset:

• MNIST Dataset:

  • A collection of hand-written digits (0 to 9), widely used for introductory image classification tasks.

• Cat vs Dogs Dataset:

  • An image dataset containing pictures of cats and dogs, commonly used for more complex image classification challenges.

Learning Objectives:

Upon completing this project, you will achieve the following learning objectives:

1. Understand Conv2D and MaxPooling Layers:

   • Gain a comprehensive understanding of the Conv2D and MaxPooling layers, fundamental building blocks in Convolutional Neural Networks (CNNs).

2. Build a Simple ConvNet for Image Classification (MNIST Dataset):

   • Learn to construct a simple ConvNet for image classification using the MNIST dataset, which involves recognizing hand-written digits.

3. Build Another ConvNet for Image Classification (Cats vs Dogs Dataset):

   • Extend your knowledge by building another ConvNet for image classification, this time using the Cats vs Dogs dataset, a more complex real-world dataset.

4. Apply Data Augmentation:

   • Explore and apply data augmentation techniques to enhance the diversity of the training dataset, leading to improved model generalization.

Files Contained in the Project:

• CNN_For_Image_Classification.ipynb: Jupyter notebook containing the project implementation and experimentation.

Usage:

1. Clone the repository:

git clone https://github.com/Praveen76/Build-a-CNN-for-Image-Classification.git
cd Build-a-CNN-for-Image-Classification

2. Open and explore the Jupyter notebook CNN_For_Image_Classification.ipynb to understand the project implementation.

3. Execute the code cells within the notebook to experiment with building CNNs for image classification using the MNIST and Cats vs Dogs datasets.

4. Gain hands-on experience with Conv2D, MaxPooling layers, and data augmentation techniques.

Feel free to contribute, report issues, or suggest improvements!

Contributing

If you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.

Code of Conduct

Please adhere to our Code of Conduct in all your interactions with the project.

License

This project is licensed under the MIT License.

Contact

For questions or inquiries, feel free to contact me on Linkedin.

About Me:

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.