This project dives into the world of image classification using Convolutional Neural Networks (CNNs). My goal is to develop a model capable of recognizing hand gestures representing the classic game of rock-paper-scissors. TensorFlow, a powerful machine learning framework, will be my platform, and Keras, its high-level API, will streamline the model building process.
TensorFlow, an open-source library by Google, empowers users with a comprehensive platform for numerical computation, machine learning, and particularly deep learning. It offers a robust set of tools for complex mathematical operations and facilitates building intricate deep learning models inspired by the human brain. Notably, TensorFlow embraces flexibility by supporting various programming languages and extends its reach beyond model creation by providing deployment tools for real-world applications on servers, mobile devices, and even web browsers. Click here!
- Machine Learning Focused: TensorFlow offers a wide range of tools and APIs specifically designed for machine learning tasks, including data preparation, model building, training, and deployment.
- Flexible Architecture: The library supports various programming languages like Python, C++, and Java, allowing developers to choose the language that best suits their needs.
- High-Level APIs: Keras, a high-level API built on top of TensorFlow, simplifies model building with a user-friendly interface.
- Production Deployment: TensorFlow offers tools and libraries for deploying trained models in various environments, from servers and mobile devices to web browsers and edge devices.
- Convolutional Neural Network (CNN): A deep learning architecture specifically designed for image recognition. It extracts features from images through convolutional layers and learns to differentiate between classes.
- Adam Optimizer: An optimization algorithm that efficiently updates the weights and biases within the CNN during training, improving the model's accuracy.
- Image Data Generator: A tool from Keras for data augmentation. It artificially creates variations of existing images (flips, rotations, etc.) to increase the training dataset size and improve model generalization.
- GitHub: @rahfianugerah
Special Thanks to Dicoding
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Several publicly available datasets were utilized for this project. Descriptions and access information for each dataset can be found in the references section (Dicoding, Kaggle). These datasets provided the foundation for my exploration of this project.