Table 8 Examples of open sources for AI algorithms
From: Artificial intelligence tool development: what clinicians need to know?
No | Open-source algorithm | Characteristics and examples |
|---|---|---|
1. | TensorFlow | Developed by Google Brain Team. It is well-suited for complex deep learning tasks and large-scale projects. Consider using TensorFlow for tasks such as image classification, natural language processing and reinforcement learning. Widely used for deep learning tasks such as medical imaging analysis, genomics and clinical natural language processing |
2. | PyTorch | Developed by Facebook’s AI Research lab (FAIR). PyTorch offers dynamic computational graphs, making it suitable for research and experimentation. Consider PyTorch for prototyping and implementing cutting-edge deep learning models. Leverage PyTorch’s flexibility and simplicity for rapid model iteration and debugging. PyTorch is popular in academia and research due to its ease of use and Pythonic syntax |
3. | scikit-learn | Built on top of NumPy, SciPy and matplotlib. It is ideal for traditional machine learning tasks such as classification, regression and clustering. Consider scikit-learn for projects with structured data and well-defined features. Provides simple and efficient tools for data mining and data analysis tasks such as disease prediction, diagnosis and drug discovery |
4. | Keras | High-level neural networks API, running on top of TensorFlow or Theano. It offers a user-friendly interface for building and training neural networks, and simplifies the process of building, training and deploying neural networks. Take advantage of Keras’s simplicity and modularity to iterate quickly on model architectures and hyperparameters. Keras seamlessly integrates with TensorFlow for production deployment |
5. | Apache MXNet | Developed by Apache Software Foundation. It supports multiple programming languages including Python, R, Scala, and Julia. Known for its scalability, efficiency and flexibility, making it suitable for projects requiring scalability and performance optimisation across multiple programming languages such as in distributed deep learning and model serving in production environments |