Difficulties in fields such as computer vision and reinforcement learning
Posted: Thu Feb 06, 2025 3:17 am
First, the computational cost of deep learning technology is very high. This makes it very difficult to train large-scale neural networks and requires huge amounts of time and resources. Under the hardware conditions at the time, it was difficult to conduct large-scale deep learning experiments, which greatly limited the development of deep learning technology.
Secondly, insufficient data volume is also an important issue. Deep learning technology requires a large amount of data for training, but the data set size at that time was relatively small and could not meet the needs of deep learning technology. This also limited the application of deep learning technology in practical applications.
In addition, since deep learning technology performed poorly in lithuania mobile database the application scenarios at the time, many researchers turned their attention to other machine learning techniques, such as support vector machines and decision trees.
In general, the 1990s and early 2000s marked the end of deep learning technology. Deep learning technology was limited in terms of computing cost, data volume, and application scenarios, and many researchers were pessimistic about its prospects. However, it was this period of lows that laid the foundation for the subsequent recovery and development of deep learning technology, prompting researchers to continuously explore and improve deep learning technology.
Secondly, insufficient data volume is also an important issue. Deep learning technology requires a large amount of data for training, but the data set size at that time was relatively small and could not meet the needs of deep learning technology. This also limited the application of deep learning technology in practical applications.
In addition, since deep learning technology performed poorly in lithuania mobile database the application scenarios at the time, many researchers turned their attention to other machine learning techniques, such as support vector machines and decision trees.
In general, the 1990s and early 2000s marked the end of deep learning technology. Deep learning technology was limited in terms of computing cost, data volume, and application scenarios, and many researchers were pessimistic about its prospects. However, it was this period of lows that laid the foundation for the subsequent recovery and development of deep learning technology, prompting researchers to continuously explore and improve deep learning technology.