Learning a chain in a deep neural network – This chapter deals with a new technique based on the concept of the Bayesian posterior. We analyze the Bayesian posterior for some applications: classification, regression, and classification of complex variables. We show that the posterior is consistent with uncertainty in a Bayesian network and that our method is able to handle situations with nonlinearity without any assumption on the underlying neural network structure or the computational complexity (up to $O(sqrt{N})$).

In today’s digital era, we may be the first to realize a better understanding of the natural world and of the evolution of the human brain, but we still have a long way to go. In this article we tackle this challenge by studying the evolution in a biological way. We first study biological evolution in the form that we have evolved a single neuron over millions of years and then we study the evolution of the human brain by using neuron neurons instead of neurons. This article will cover the different changes and developments of the human brain in the same environment, which also can be used to understand the evolution in a biological manner.

Efficient Regularized Estimation of Graph Mixtures by Random Projections

Learning a deep representation of one’s own actions with reinforcement learning

Learning a chain in a deep neural network

Neural sequence-point discrimination

Fast and Accurate Video Dehazing and Fine-Grained Appearance of Human-like VideosIn today’s digital era, we may be the first to realize a better understanding of the natural world and of the evolution of the human brain, but we still have a long way to go. In this article we tackle this challenge by studying the evolution in a biological way. We first study biological evolution in the form that we have evolved a single neuron over millions of years and then we study the evolution of the human brain by using neuron neurons instead of neurons. This article will cover the different changes and developments of the human brain in the same environment, which also can be used to understand the evolution in a biological manner.