Actually, it is a combination of two models – AR and MA. Let me make it clear. Auto Regressive means when there is some correlation between values in a time series and the values that precede and succeed them. In a model, the value of AR determined by partial auto correlation which is the partial autocorrelation function gives the partial correlation of a time series with its own lagged values. A moving-average model is conceptually a linear regression of the current value of the series against current and previous (observed) white noise error terms.

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, autonomous locomotion and board game programs, where they have produced results comparable to and in some cases superior to human experts. With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences. Nevertheless, deep learning is one of the most talked-about topics in the domain of computer science and technology.

I hope that you have liked my previous posts. Did you get some interest in it? Well, I would expect your answer "yes!". Let's start the first thing first. Before jump into the sea please keep your equipments ready.

I hope you liked my previous posts. Today I am going to talk on an important topic; that is Probability Distribution. Well, it's like you are trying to assume or predict something in a scientific way. Now the question is Probability on what? First concentrate on 2 kind of variables: 1. Discrete (Example like coin toss, rolling die, counting person etc.) Now the question is why do we need to learn probability distribution?

Let's start today's topic – Types of Statistics. Before directly jumping into analysis first of all you need to arrange the data, format the data and try to get the basic structure of the sample data. Here the central of tendency and variance come into play for a major role. Is my data properly arranged? What is the behaviour of the data?

Let's start today's topic – Types of Statistics. Before directly jumping into analysis first of all you need to arrange the data, format the data and try to get the basic structure of the sample data. Here the central of tendency and variance come into play for a major role. Is my data properly arranged? What is the behaviour of the data?

I hope you liked my previous posts. Today I am going to talk on an important topic; that is Probability Distribution. Well, it's like you are trying to assume or predict something in a scientific way. Now the question is Probability on what? First concentrate on 2 kind of variables: 1. Discrete (Example like coin toss, rolling die, counting person etc.) Now the question is why do we need to learn probability distribution?