Firstly, you need to understand little bit about what is Traditional Programming. Traditional Programming is one of the simplest programming system. You are one who creates the program which then process information according to condition/rules defined by you and you get the output. Let's take an example of traditional programming that you can understand clearly. In this diagram we can see Traditional Programming.

In the modern banking world new technologies play a widely reported role in anti-money laundering (AML) protocols preventing financial crime – however it is important that we do not overlook technology's potential for establishing financial innocence. To businesses and institutions operating in and between developed markets, whose international transactions are fast and painless, this sentiment may seem counter intuitive. AML compliance is necessary for regulatory reasons, and catching out bad actors is, of course, a primary goal of any business – but why should we view AML technology through the lens of establishing innocence? This is a question which emerging market corporates will have no difficulty answering if they have ever attempted to interface with counterparts in developed markets. Entities based in emerging markets are often tarred with the brush of AML risk due to their geography and unrelated to their specific business, and – consequently – such organisations find international transactions lengthy, arduous and expensive as they navigate an AML compliance process that operates from a base level that is an unfair assumption of their risk.

Machine learning (ML) is a part of artificial intelligence (AI) that teaches the computer to work and make decisions based on historical data. A ML algorithm learns from historical data to generate a predictive model used to forecast the future outcome. Advanced forms of ML models could be applied in AI applications, such as Recommender System, Text Processing and Image Recognition. To work with ML, a data scientist should have a good knowledge of mathematics and statistics, and the ability to process data and interpret the results. To process the data, you have to use specific tools or be able to program.

AI ethics is a hot topic these days, so you see all kinds of rhetoric zooming around. Complaints range from "the robots took my job" to "your computer system is just as biased as you are (you jerk)." Why aren't we talking about what makes ML/AI uniquely more dangerous than other technologies? The topics that come up in connection with AI ethics are vital, timely, and necessary. I just wish we wouldn't use the term AI ethics whenever it… isn't even about AI.

The term neural network doesn't need an introduction at all, only a few know the power of a neural net and a lot of people wanna learn this extraordinary tech. What are you going to read here then? Rather than discussing the types and applications of the neural network, I will be going through the seven mechanisms of the neural network that makes it powerful and versatile. This is an important question one has to answer before diving into this, if we unravel the caveats of a traditional program and replace it with some cool tweaks then we can call this a neural network. In traditional programming, we usually write the steps sequentially for it to run.

If I see a new out a new observation I can say if this observation relates to this particular logic ... Read moreWhat is the importance of logic statements in Machine Learning?

Mashable's series Algorithms explores the mysterious lines of code that increasingly control our lives -- and our futures. An algorithm is a simple concept that, today, has many complex manifestations. Algorithms' central and opaque position at the heart of social networks like Facebook cause some to view algorithms in general with a sort of mystical reverence. Algorithms have become synonymous with something highly technical and difficult to understand, that is either an arbiter of objective truth, or, on the other end of the spectrum, something wholly untrustworthy. But when people refer to "the algorithm" -- whether Facebook's or another tech company's recommendation algorithm, or just "algorithms" in general -- do they really know what it means?

Roughly a year ago, we wrote "What machine learning means for software development." In that article, we talked about Andrej Karpathy's concept of Software 2.0. Karpathy argues that we're at the beginning of a profound change in the way software is developed. Up until now, we've built systems by carefully and painstakingly telling systems exactly what to do, instruction by instruction. The process is slow, tedious, and error-prone; most of us have spent days staring at a program that should work, but doesn't. And most of us have been surprised when some program that has been reliable for some time suddenly screws up at some slightly unexpected input.

AI ethics is a hot topic these days, so you see all kinds of rhetoric zooming around. Complaints range from "the robots took my job" to "your computer system is just as biased as you are (you jerk)." Why aren't we talking about what makes ML/AI uniquely more dangerous than other technologies? The topics that come up in connection with AI ethics are vital, timely, and necessary. I just wish we wouldn't use the term AI ethics whenever it… isn't even about AI.

When I try to introduce the concept of AI DApps, I often find that it is particularly difficult when people lack an accurate grasp of what machine learning is. There is an overwhelming amount of information online about machine learning targeted toward audiences with different levels of technical expertise. In this series, I introduce machine learning at different technical levels, with the aim of providing a basic framework that helps you understand machine learning, regardless of your background, starting at the highest level. In traditional programming, programmers write programs, which are made of lines of code that instruct computers to perform certain tasks. For example, a programmer can write a program to detect whether the word "book" exists in a news article.