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Journey Through Deep Learning Frameworks
This chapter follows the speaker's transition from traditional machine learning to deep learning, focusing on their development of the MXNet framework. It explores the evolution of deep learning paradigms, examining optimization techniques in computational graphs and the impact of projects like TVM on machine learning. The discussion highlights the ongoing advancements in model portability and infrastructure necessary for efficient deployment of language models on edge devices.