We assess framework performance, code quality, and implementation complexity through hands-on development of real-world projects
Every framework is tested across multiple use cases including computer vision, NLP, time series analysis, and reinforcement learning
We benchmark each framework's training speed, inference performance, memory usage, and scaling capabilities
We evaluate documentation quality, community size, issue resolution, and the ecosystem of available tools and plugins
We test deployment capabilities across cloud platforms, edge devices, mobile environments, and enterprise systems
We analyze how quickly developers can become productive with each framework based on complexity and available learning resources
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