Future directions in deep learning
Abstract
The rapid development of deep learning technology is attributed to the collaborative advancement of algorithms, data, and computing power. From early backpropagation algorithms to today’s Transformer architecture, deep learning models have continuously optimized their structural design and training methods, significantly improving their performance and generalization ability. At the same time, the accumulation of massive data and the popularization of efficient computing resources provide a solid foundation for the application of deep learning technology. Deep learning technology has not only attracted widespread attention in academia, but also spawned numerous innovative applications in industry. Its evolution path not only promotes the progress of artificial intelligence technology, but also provides important support for the digital transformation of various industries. Based on this, the following discusses the evolution path and cutting-edge development of deep learning technology for reference.
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