Understanding Basic ML & AI Libraries: Tensorflow, JAX, & PyTorch

Machine Learning (ML) and Artificial Intelligence (AI) have revolutionized the way we approach data analysis and problem-solving. In the world of ML and AI, there are several libraries that are commonly used to build and train models. Three of the most popular libraries are TensorFlow, JAX, and PyTorch.

TensorFlow

TensorFlow is an open-source ML library developed by Google. It allows users to build and train deep learning models using a high-level API. TensorFlow is known for its flexibility and scalability, making it a popular choice for both research and production environments.

JAX

JAX is another ML library that has gained popularity in recent years. Developed by Google Research, JAX is built on top of the NumPy library and provides a combination of automatic differentiation and hardware acceleration. JAX is known for its speed and ease of use, making it a popular choice for researchers and developers alike.

PyTorch

PyTorch is another popular ML library developed by Facebook. It is widely used for building and training deep learning models, especially in research and academia. PyTorch is known for its dynamic computation graph, which allows for more flexibility in model building compared to static computation graphs used in other libraries like TensorFlow.

In conclusion, understanding the basic functionalities of TensorFlow, JAX, and PyTorch is essential for anyone working in the field of ML and AI. Each of these libraries has its own strengths and weaknesses, so it is important to choose the right one based on your specific needs and requirements.