A study on different deep learning algorithms used in deep neural nets:MLP SOM and DBN

Deep learning is a wildly popular topic in machine learning and is structured as a series of nonlinear layers that learn various levels of data representations. To implement various computer models, deep learning employs numerous layers to represent data abstractions. Deep learning approches like generative, discriminative models and model transfers approaches have transformed information processing. This article proposes a comprehensive review of various deep learning algorithms Multi layer perception (MLP), Self-organizing map (SOM) and deep belief networks (DBN) algorithms. It first briefly introduces historical and recent state-of-the-art reviews with suitable architectures and implementation steps. Then the various applications of those algorithms in various fields such as speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications, etc are classified.