Semi-supervised learning seeks to build accurate classification machines by taking advantage of both labeled and unlabeled data. This learning scheme is useful especially when labeled data are scarce while unlabeled ones are abundant. Among the existing semi-supervised learning algorithms, Laplacian support vector machines (SVMs) are known to be particularly powerful but their success is highly dependent on the choice of kernels., In this paper, we propose an algorithm that designs kernels as a part of Laplacian SVM learning. The proposed kernels correspond to deep multi-layered combinations of elementary kernels which capture simple - linear - as well as intricate - nonlinear - relationships between data. Our optimization process finds both the parameters of the deep kernels and the Laplacian SVMs in a unified framework resulting into highly discriminative and accurate classifiers. When applied to the challenging ImageCLEF2013 Photo Annotation benchmark, the proposed deep kernels show significant and consistent gain compared to existing elementary kernels as well as standard multiple kernels.