"深度学习中的受限玻尔兹曼机网络原理与实践解析"

Restricted Boltzmann Machine (RBM) is a type of neural network that is defined by an energy function and trained using contrastive divergence algorithm (CD-K). It is widely used in feature engineering in the field of deep learning. Additionally, RBM can be stacked to form Deep Belief Networks, which serves as the starting point of the rise of deep learning algorithms with significant historical importance and wide applications. In this chapter, we will first introduce the mathematical principles of Restricted Boltzmann Machine and then implement a simple RBM using Theano framework for image feature extraction, providing us with an intuitive understanding of RBM. RBM is unique compared to traditional neural networks as it is a fully connected network without hierarchical layers. Originating from Hopfield network, RBM is trained to minimize the energy function, with applications in various fields such as the traveling salesman problem. In summary, Restricted Boltzmann Machine is a powerful tool in deep learning for feature extraction, with the ability to form Deep Belief Networks and has played a crucial role in the development of deep learning algorithms. Through this chapter, we have gained a deeper understanding of RBM and its applications through practical implementation, laying the foundation for further exploration and application of RBM in the field of deep learning.