Reinforcement learning requires a large amount
Posted: Thu Feb 06, 2025 3:17 am
In addition to the limitations of computational cost and data volume, deep learning technology also performed poorly in the application scenarios at the time, especially in fields such as computer vision and reinforcement learning. In the field of computer vision, the traditional methods at the time mainly relied on manually designed feature extractors, and the application of deep learning technology in this field had not yet been widely recognized. In the 2001 ImageNet large-scale visual recognition competition, deep learning technology performed quite poorly and had no obvious advantages over other machine learning technologies, which also intensified doubts about the prospects of deep learning technology.
In the field of reinforcement learning, deep learning technology lithuania mobile database also faces many challenges. of training data and computing power, but the technology at the time could not meet these requirements. In addition, the algorithms and applications of reinforcement learning were not mature enough, which put forward higher requirements for deep learning technology.
In general, the 1990s and early 2000s were the low point of deep learning technology. Fields such as computer vision and reinforcement learning put forward higher requirements for deep learning technology, but the technology and applications at that time could not meet these requirements. With the increase of computing power and data scale and the improvement of algorithms, deep learning technology has gradually demonstrated its powerful capabilities in these fields and has gradually attracted more attention.
In the field of reinforcement learning, deep learning technology lithuania mobile database also faces many challenges. of training data and computing power, but the technology at the time could not meet these requirements. In addition, the algorithms and applications of reinforcement learning were not mature enough, which put forward higher requirements for deep learning technology.
In general, the 1990s and early 2000s were the low point of deep learning technology. Fields such as computer vision and reinforcement learning put forward higher requirements for deep learning technology, but the technology and applications at that time could not meet these requirements. With the increase of computing power and data scale and the improvement of algorithms, deep learning technology has gradually demonstrated its powerful capabilities in these fields and has gradually attracted more attention.