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Five Brownie Points On How Big Data Is Striking Our Lives Daily

  Leroy Hood  once said, “If you just focus on the smallest details, you never get the big picture r ight .” Big Data Analytics is a new technological process that rose to popularity in the early 2000s when analyst  Doug Laney  defined the 3 Vs in business analysis. He said that the strength of the data is linearly proportional to the Volume, Velocity and Variety of data collected. Soon enough, the heads of the corporate world recognized the potential of analyzing their data. However, the magnitude of data being stored and analyzed today is almost inconceivable. Anyone with knowledge can make almost any sort of prediction from all the data collected. For example, Forbes predicts that more data has been stored and structured just in the past two years than in the entire history of the previous human race. Big Data is the new money of the advanced age. As technology stays to impact our careers massively, our data dependence is also growing. It has now begun changing everything in our job
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  Best AI & ML Community On FaceBook   In the ever-changing artificial intelligence panorama, developers need to espouse every plausible way of keeping themselves modernized with the most advanced developments. There are numerous techniques like following AI and ML blogs, Communities, News, magazines and many more. Still, suppose you don't want to tour multiplied sites or sections to keep yourself updated. In that case, you have a comprehensive, consistent alternative, i.e., Facebook Groups, which is more productive than other alliances like LinkedIn Groups and Reddit threads. While such associations help keep you acquainted, one should not restrict just browsing about the newest developments. Instead, developers should involve in sharing thoughts and contributing to the community.   About Artificial Intelligence and Machine Learning Community Artificial Intelligence and Machine Learning  is a Facebook Group with higher than 174,000 members from all around the earth w
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5 Top Secret Gems Of Python Libraries Untold To The Data Science World

  Photo by krakenimages from unsplash One of the most incredible things about practicing Python is its continuity of open-source  libraries. There is a library for fundamentally anything. If you have studied some of my preceding blogs, you may have remarked that I’m a big supporter of low-code libraries. That’s not because I’m procrastinating to type code but because I fancy investing my time operating on a project with values. If a library can work a problem, why not preserve your valuable time and give it a try? Today, I will present you with five libraries you have never heard about, but you should attach them to your portfolio. Let’s get started! ----------------------------------------------------------------------------------------------------------------------------- PS: There are lots of amazing resources out there for learning ML and data science. My personal favorite is  DataCamp . This is where I started my journey and trust me it’s amazing and worth your time. So this artic
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Types of Optimization algorithms and Optimizing Gradient Descent

Have you ever wondered which optimization algorithm to use for your Neural network Model to produce slightly better and faster results by updating the Model parameters such as Weights and Bias values? Should we use Gradient Descent or Stochastic gradient Descent or Adam? I too didn’t know about the major differences between these different types of Optimization Strategies and which one is better over another before writing this article. NOTE:  Having a good theoretical knowledge is amazing but implementing them in code in a real-time deep learning project is a completely different thing. You might get different and unexpected results based on different problems and datasets.   So as a Bonus,I am also adding the links to the various courses which has helped me a lot in my journey to learn Data science and ML, experiment and compare different optimization strategies which led me to write this article on comparisons between different optimizers while implementing deep learning a
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