Designing Intelligence with Brains

Artificial Intelligence & Robotic Brain are referenced from the Human Brain, and as we understand our brain we try to develop it artificially. Human Brain is one of the very complex structure which is hard to understand, comprehend & restructure it, but it restructures itself. It consists of trillions of connections which helps in day to day task & also taking big decisions. What if this decision taking ability in some form can be transferred to Machines, Robots & computers. To develop & make that possible there must be the smallest constitute. That smallest constitute is called Perceptron.  Perceptron in simple language is artificial neuron. It works the same way as our neuron works. The meta structure of Perceptron is inspired by the biological neuron. Just like a biological neuron has dendrites to receive signals, a cell body to process them, and an axon to send signals out to other neurons, the artificial neuron has a number of input channels, a pr...

We all Know that Computers & Mobile devices getting smarter and smarter day by day, but these all are hardware. They are doing so with the help of NPU(Neural Processing Unit)  & Quantum Computing(which is in premature state right now). Apart from these two all the magic of AI & ML comes from software side like predicting Search Results whenever you start typing, predicting physical objects, predicting Human mood by analyzing facial pattern. These all are very fascinating and out of the world things which was only imagination 20-30 years before & now we use them on daily basis. So here is two popular & widely used algorithm. Supervised Learning:- Supervised learning is an approach to Machine Learning that is based on training data that includes expected answers. An Artificial Intelligence uses the data to build general models that map the data to the correct answer. Supervised learning is where you have input variables (x) and an output variable (Y) and...

Linear Regression is one of the best & popular algorithms in Machine Learning for predicting the output of situations before it even occurred. To understand Linear Regression first we have to understand what is Regression problem? What is Regression Problem? Problems occurred in predicting real-valued-output for different-different inputs is called Regression problem. Linear Regression Linear regression is Linear model. i.e, models that assume a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression. Different techniques can be used to prepare or train the linear regression equation from data, the most common of ...