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Recently , I started reading a book  Structures : Or Why Things Don't Fall Down by J. E. Gordon. This book talks about how life has evolved right from the age of when life was unicellular and completely dependent on water for existence. I particularly like the section where structure is defined as something which can sustain load. This book was originally published in year 1978. I wrote a small summary of the first chapter just to make sure that my thoughts are coherent around this matter. I am sharing the summary here. As, we all know if the engineering structures breaks, then people are more likely to get killed . Obviously, we don't want that to happen. Engineers do well to investigate the reason behind the fall of the structure. But the problem is when they share the reason why did that happen they talk in a very strange language which doesn't seem to like an interesting one from the get-go. But, well structures will remain part of our lives  forever that we ca

For the past year , we’ve compared nearly 8,800 open source Machine Learning projects to pick Top 30 (0.3% chance). This is an extremely competitive list and it carefully picks the best open source Machine Learning libraries, datasets and apps published between January and December 2017. Mybridge AI evaluates the quality by considering popularity, engagement and recency. To give you an idea about the quality, the average number of Github stars is 3,558. Do visit our Hotel price comparison api which compares more than 200 hotel websites to get you the best possible price of your dream hotel. Python Projects of the Year (avg. 3,707 ⭐️): Here (0 duplicate) Learn Machine Learning from Top Articles for the Past Year: Here (0 duplicate) Open source projects can be useful for data scientists. You can learn by reading the source code and build something on top of the existing projects. Give a plenty of time to play around with Machine Learning projects you may

We need to get smarter about emerging technologies, such as artificial intelligence, robotics and blockchain. What triggered this thought was a visit to an industrial factory last week. We all know that something is happening. And everyone seems to agree that our future will be automated. But, we tend to believe that it will only — or mainly — affect repetitive “manual labor”. Automation of “knowledge work” is not on many people’s agenda. But, is this correct? Or, is it a naïve view that will be detrimental to business and society? The factory visit made me think about these issues and what “knowledge workers” — executives, managers, advertisers, lawyers, accountants, etc. — need to do to remain relevant in the coming new world. Don't forget to check out our Hotel Price Comparison API which compares more than 200 hotel websites. The “Disappearance” of Manual Labour The visit was an eye-opening experience. I will not go into details, but the factory

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen. My lecturer is a full-time Applied Math and CS professor at the Technical University of Denmark, in which his research areas are logic and artificial, focusing primarily on the use of logic to model human-like planning, re