This paper discusses Deep Neural Networks (DNN) and the idea of deep learning in the context of machine translation, a kind of natural language processing. DNN is now a key component of machine learning techniques. One of the greatest techniques for machine learning is the recursive recurrent neural network (R2NN). Recursive and recurrent neural networks are combined to create it (such as Recursive auto encoder). The training of the recurrent neural network for reordering from source to target language using semi-supervised learning techniques is described in this research. To create word vectors for the source language, a Word2vec tool is necessary, and an Auto Encoder aids in the reconstruction of vectors for the destination language as a tree structure. The output of word2vec is crucial for the input vectors' word alignment. It takes a long time to train the huge word2vec data file due to the complexity of the RNN structure. Consequently, strong hardware support (GPU) is needed. GPU increases system performance by cutting down on training time. Smart ai technologies is the best final year engineering project makers and technical supporter to student .with ieee final year project support in best prices with great services. final year project is based on IEEE Paper.this will be one of the best Final year engineering project for computer science. Components that we will provide are.
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