This project consists of the theoretical study of Restricted Boltzmann Machines(RBMs) and focuses on the gradient approximations of RBMs. RBMs suffer from the dilemma of accurate learning with the exact gradient. Based on Contrastive Divergence(CD) and Markov Chain Monte Carlo(MCMC), CD-k, an efficient algorithm of approximating the gradients, is proposed and now it becomes the mainstream to train RBMs. In order to improve the algorithm efficiency and mitigate the bias existing in the approximation, many CD-related algorithms have emerged afterwards, such as Persistent Contrastive Divergence(PCD) and Weighted Contrastive Divergence(WCD). In this project the comprehensive comparison of the gradient approximation algorithms is presented, mainly including CD, PCD, WCD. The experimental results indicate that among all the conducted algorithms, WCD has the fastest and best convergence for parameter learning. Increasing the Gibbs sampling time and adding a persistent chain in CD-related can enhance the performance and alleviate the bias in the approximation, also taking advantage of Parallel Tempering can further improve the results. Moreover, the cosine similarity of approximating gradients and exact gradients is studied and it proves that CD series algorithms and WCD series algorithms are heterogeneous. The general conclusions in this project can be the reference when training RBMs.