The resurgence of deep learning in the mid-2000s to 2010
In 2006, Geoffrey Hinton and other pioneers of deep learning technology proposed a new type of deep neural network model, namely Deep Belief Networks DBN. Deep belief networks are an unsupervised learning method that can be used to learn the potential distribution of data and can be used for tasks such as data generation, noise reduction, and classification. Unlike traditional feedforward neural networks, deep belief networks use a method called "greedy layer-by-layer pre-training" to train the network layer by layer so that each layer can capture the characteristics of the data well and gradually combine into a higher-level feature representation. Then, the backpropagation algorithm can be used for fine-tuning and optimization to improve the accuracy of the network.
The introduction of deep belief networks is considered an lithuania mobile database important breakthrough in deep learning, laying the foundation for the revival of deep learning technology. Since then, deep learning technology has been widely used in computer vision, natural language processing and other fields, such as DBN-based image classification, speech recognition, machine translation and other tasks. The progress of deep learning technology has also attracted the attention and investment of more researchers, thus promoting the continuous development and innovation of deep learning technology.
Introducing the breakthrough of convolutional neural networks
In the process of the gradual recovery of deep learning technology, the development of convolutional neural networks CNNs is also a very critical step. Convolutional neural networks are a special neural network structure that can retain the spatial structure information of data when processing two-dimensional or three-dimensional data, and can effectively process large-scale image, video and other data.