These days, lots of companies are implementing the blockchain technology with the hope of discovering opportunities to create differences in their regular business process. Unfortunately, a lot of these implementations never get past of the production stage. While the technology comes with lots of potential enough to attract business leaders, there’s a significant gap between that hype and market reality. If you too are thinking of implementing blockchain technology, here’re nine of the most common mistakes and ways to avoid them.
Undeniably, has become one of the most talked-about areas of the IT domain. The demand for developers is growing rapidly and professionals from different industries, as well as, beginners are trying to step into this field.
Though there’re people who imagine a future where machines replace humans, it’s probably the best time to learn this technology as it’ll change the future of the tech domain drastically.
If you’re a beginner and looking to become an artificial intelligence developer, here’re the most effective ways you should follow.
Among all the capabilities of artificial intelligence, probably the biggest one is its ability to extract meaning from human language. And natural language processing (NLP) is the subset of artificial intelligence that makes this possible. You can consider natural language processing as a method for machines to analyze, understand, and obtain meaning from human language in an effective and smart way.
While natural language processing has been around for a long time, the emergence of big data together with the availability of enhanced algorithms and powerful computing, the technology is experiencing rapid advancements in recent years.
Natural language processing comprises…
As organizations and businesses have started to realize that there’s a huge value hiding in the massive amount of data they capture on a regular basis, they’ve been trying to employ different techniques to realize that value. While the ultimate goal is to produce actionable insights from that data, the tech world is getting filled with a significant number of technical terms. And among all these terms, probably the most talked-about terms are data science and data mining. Though some people use them interchangeably, they come with significant differences. Here’re seven most prominent differences between data science and data mining.
These days, as the world is getting more and more connected through different types of digital devices, a massive volume of data is getting emanated from a huge number of digital sources. Businesses and organizations from across the globe are leveraging the power of this data and putting it to their advantages.
Big data analytics is performed to identify correlations, hidden patterns, and to derive actionable insights that can help businesses make informed decisions.
While the concept of big data has been around for a significant number of years, everything has started to change with the emergence of big data…
If you’re interested in learning artificial intelligence or machine learning or deep learning to be specific and doing some research on the subject, probably you’ve come across the term “neural network” in various resources. In this post, we’re going to explore which neural network model should be the best for temporal data.
You can consider an artificial neural network as a computational model which is based on the human brain’s neural structure. Neural networks are capable of learning to perform tasks such as prediction, decision-making, classification, visualization, just to name a few.
An artificial neural network contains processing elements or…
In the U.S., over 36,000 weather forecasts are issued every day that cover 800 different areas and cities. Though some people may complain about the inaccuracy of such forecasts when a sudden spell of rain messes with their picnic or outdoor sports plan, not many spare a thought about how accurate such forecasts often are. That’s exactly what the people at Forecastwatch.com (a leader in climate intelligence and business-critical weather) did. They assembled all 36,000 forecasts, placed them in a database, and compared them to the actual conditions that existed on that particular day in that specific location. …
While the concept of big data isn’t new, most businesses have recently realized that if they can capture all the data which streams into their operations, analytics can be applied and significant value can be derived from that. Now, the massive amounts of data only become useful when big data analytics is performed to identify patterns and insights that would be left undiscovered otherwise. As a result, businesses are increasingly looking for professionals who’re familiar with various big data analytics tools to get help in attaining their goals. Here’s an overview of some popular big data analytics tools.
These days, the business world runs entirely on data and none of the companies can survive without data-driven strategic plans and decision making. The field of data science is quite broad and contains a significant number of job positions including data scientist and data engineer. If you want to step into the data science field, it’s crucial to understand the differences between a data scientist and data engineer to identify whether it’d be possible for you switch positions without investing much effort and time.
In this post, we’ve tried to outline the key differences between these two…