Google's brain team is open sourcing Tensor2Tensor, a new deep learning library designed to help researchers replicate results from recent papers in the field and push the boundaries of what's possible by trying new combinations of models, datasets and other parameters.
Source: TCIf you want to work on J.P. Morgan's Quantitative and Derivative Strategy team within the bank's Global Research group, then you better have a PhD in one of three areas: applied mathematics, computer science and statistics.
Source: efinancialcareersIn the tech industry, new skills and roles emerge faster than traditional education can keep up with. A recent example is the field of data science and the associated profession, Data Scientist.
Source: CodementorIt is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
Source: HOBMachine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Source: HOBThe most important thing you must know if you want to get succeed as a Machine Learning engineer is how you should deal with the most precious thing called "DATA". Data analysis is the most important thing that you need to master in order to proceed with Machine learning. Although it may sound surprising, unless you are able to analyze the data correctly, you cannot build a model to use on the data. Now Data analysis is a pretty big field in itself and to work on data analysis.
Source: HOBThe essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.
Source: HOBIn this article you will read about 15 Android applications which can be useful for a data scientist or data analyst.
Source: HOBThis article list data sets from the data science world that you might find interesting.
Source: HOBWe covered 50 data sets for data scientists that are amusing in part 1. In part two we cover 50 more of those.
Source: HOB50 data sets that data scientist find amusing.
Source: HOBRegardless of where you stand on the matter of Data Science sexiness, it's simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it.
Source: HOBThe two most powerful technologies Data Science and Machine Learning are not only changing the industries but also influencing many movies-makers across the globe. Basically these technologies are penetrating the entire world.
Source: HOBGetting learners to read textbooks and use other teaching aids effectively can be tricky. Especially, when the books are just too dreary.
Source: HOBBooks are very useful resource for the learners and every learner always search for the best books to gain insights and here we have the collection of popular deep learning books which are recommended by experienced professionals to read.
Source: HOBComputer Science is quite an interesting course. Many people study it with the hope of being the next big computer programmer, become a hacker, system administrator among other lucrative careers. As much as it may sound an interesting course, there is one thing that most people donā??t like it: The close relationship between computer science and mathematics.
Source: HOBDeirdre Dempsey has decades of experience dealing with data analytics, data science stemming from a strong foundation in mathematics.
Source: HOBMachine learning offers organizations the potential to make more accurate data-driven decisions and to solve problems. Now organizations are leveraging the machine learning technology and this is not the magic it presents many of the same challenges as other analytics methods.
Source: HOBThis article serves as a quick guide on one of the most important theorems that every data scientist should know, the Central Limit Theorem.
Source: HOBStatistics is difficult. Of course it is, as mostly that's the actual science part in Data Science. But it doesn't mean that you couldn't learn it by yourself if you are smart and determined enough. These books are really good for setting your mind to think more numerical, mathematical and statistical. They also present why statistics is exciting really well.
Source: HOBProgramming is an integral part of data science. Among other things, it is acknowledged that a person who understands programming logic, loops and functions has a higher chance of becoming a successful data scientist.
Source: HOBNatural language processing, one of the most exciting components of AI is all set to rule the way we communicate with the external world. Natural Language Processing uses computational and mathematical methods to analyze the human language to facilitate interactions with computers using conversational language.
Source: HOBMachine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.
Source: HOBMachine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.
Source: HOBIf you are a computer science graduate or someone who is thinking to make a career in software development world or an experienced programmer who is thinking about his next career move but not so sure which field you should go then you have a come to the right place.
Source: HOBThe Data Science candidates hold a strong background in statistics and mathematics is the only criteria of being getting selected at Google. Not just Google, other top companies (Amazon, Airbnb, Uber etc) in the world also prefer candidates with strong fundamentals rather than mere know-how in data science. If you are also interested to work with top brands than it is essential to develop your mathematical understanding of data science.
Source: HOBData visualization plays a very important part when it comes to communicating the results to the executives. You may have all your fancy formulae and other mechanics at coming to the results, but the executives want to see the results which make sense to them. From the raw data that you have from the various sources, you can get out with certain statistical properties and the sort of analysis that you do and that can then finally be represented in a really meaningful and very precise visual form.
Source: HOBThe selection process of data scientists at Google gives higher priority to candidates with strong background in statistics and mathematics. Not just Google, other top companies (Amazon, Airbnb, Uber etc) in the world also prefer candidates with strong fundamentals rather than mere know-how in data science.
Source: HOBAll professionals I'm sure looking ahead to a new start and want to increase their data analysis skills. So here is the collection of books through which data scientist can sharpen up their knowledge and skills.
Source: HOBIf you plan to start learning machine-learning models then you'll need a reasonably deep knowledge of math, spanning linear algebra, calculus, and statistics. But for beginners in the field, learning the basics of programming and getting to grips with a language like Python, which is commonly used for machine-learning tasks.
Source: HOBHowever, you soon figure out that it is infinitely more challenging to hire a data scientist compared to a software developer, perhaps because of the following three reasons.
Source: HOBThe term Robotic Process Automation (RPA) is drawing more and more attention nowadays and has put people in a dilemma that whether it is right to use it or not. The use of software along with machine learning and artificial intelligence to manage high-volume repetitive tasks is termed as Robotic Process Automation.
Source: HOBToday, the most desirable career option seems to be a data scientist and machine learning, as every individual either it is a college-going student or a professional is looking to switch their career onto data science.
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