Development Philosophy TensorFlow takes a production-first approach, emphasizing scalability, deployment, and enterprise features. Originally built around static computational graphs, though TensorFlow 2.0 introduced eager execution by default. PyTorch prioritizes research flexibility and intuitive development. Built from the ground up with dynamic computational graphs and a “Pythonic” design philosophy that feels natural to Python developers. EaseContinue reading “TensorFlow vs. PyTorch”
Tag Archives: machine-learning
AI Agent Loop
Option One An AI Agent Loop refers to the cyclical process by which an autonomous AI agent perceives its environment, plans actions, executes those actions, and reflects on the results. This loop enables the agent to operate intelligently in dynamic environments by continually adapting its behavior based on feedback and outcomes. It is foundational toContinue reading “AI Agent Loop”
Notes from San Francisco 