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With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

To solve the problem that collaborative filtering algorithm only uses the user-item rating matrix and does not consider semantic information, we proposed a novel collaborative filtering recommendation algorithm based on knowledge graph. Using the knowledge graph representation learning method, this method embeds the existing semantic data into a low-dimensional vector space. It integrates the semantic information of items into the collaborative filtering recommendation by calculating the semantic similarity between items. The shortcoming of collaborative filtering algorithm which does not consider the semantic information of items is overcome, and therefore the effect of collaborative filtering recommendation is improved on the semantic level. Experimental results show that the proposed algorithm can get higher values on precision, recall, and F-measure for collaborative filtering recommendation.

ETCS Level 2 (European Train Control System Level 2, ETCS-2) has drawn particularly attention from researchers and industries. A new CPN model-based formal approach for test cases and sequences generation is proposed in this paper to increase the test automation degree of the ETCS-2 system and subsystems.In this paper, a set of modelling rules is presented firstly to make the Coloured Petri Net (CPN) model more suitable for test generation. Then, an automated test approach is described in detail, which includes an automatic test case generating algorithm and a type of automatic test sequence searching algorithm. The generated set of test cases satisfies specified coverage. The test sequence searching algorithm guarantees the results satisfying the minimum number of test sequences covering all test cases. The output of this approach is a set of well-formed XML (Extensible Markup Language) file to increase the automation degree of the test executing process. Finally, a partial model of ETCS-2 On-Board subsystem is built and analysed using the CPN Tools as a case study. The model-based formal approach is implemented on this model and the test cases and test sequences are all generated in a form of XML. The conclusion show that the CPN-model based testing approach can be used to improve the automation of the testing procedure and the generated test cases can meet the relative requirement.