Shortly about deep learning (for reference):Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by  using a deep graph with multiple processing layers, composed of  multiple linear and non-linear transformations.Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent  neural networks have been applied to fields like computer vision,  automatic speech recognition, natural language processing, audio  recognition and bioinformatics where they have been shown to produce  state-of-the-art results on various tasks.My question:Can deep neural networks or convolutional deep neural networks be viewed as ensemble-based method of machine learning? Or it is different approaches?
Do deep learning algorithms represent ensemble-based methods?
neural networks;machine learning;deep learning;convolutional neural networks
You should think of them as different approaches. A deep neural net is a single independent model, whereas ensemble models are ensembles of many independent models.The primary connection between the two is dropout, a particular method of training deep neural nets that's inspired by ensemble methods. 