Switching To Projects.

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Scalability.

Scalability ·          After a web application has been developed, there are still issues that need to be considered when deploying to the internet. A big concern is scalability. An application can work well with only a few users, but it also needs to be able to support many more people accessing data and using the application simultaneously. The vast majority of the time, there is no one best way to scale, but rather a number ways with different trade-offs that should be taken into account. Server Scaling ·          A server can only perform a finite number of tasks per second, measured in hertz. Modern servers and processors often have speeds in gigahertz. The operations being measured are low-level: adding two numbers together, for example. Due to limited server speed, a server can only respond to a limited number of users in a given second. ·        ...
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Neural Networks, Manifolds, and Topology

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Recently, there’s been a great deal of excitement and interest in deep neural networks because they’ve achieved breakthrough results in areas such as computer vision. 1 However, there remain a number of concerns about them. One is that it can be quite challenging to understand  what  a neural network is really doing. If one trains it well, it achieves high quality results, but it is challenging to understand how it is doing so. If the network fails, it is hard to understand what went wrong. While it is challenging to understand the behavior of deep neural networks in general, it turns out to be much easier to explore low-dimensional deep neural networks – networks that only have a few neurons in each layer. In fact, we can create visualizations to completely understand the behavior and training of such networks. This perspective will allow us to gain deeper intuition about the behavior of neural networks and observe a connection linking neural networks to an area of mathe...
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What is the Difference between Data Science and Software Development.

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Data science development is very different from software development, and getting the two to mesh is sometimes like trying to cobble together Tinker Toys with Lincoln Logs.     One data science expert once described the differences as: Software development is “Measure twice; cut once,” while Data Science is “Cut, cut, cut!” The methodologies and processes that support successful software development do not work for data science projects according to one simple observation:   software development  knows , with 100% assurance, the expected outcomes, while data science – through data exploration and hypothesis testing, failing and learning –  discovers those outcomes. First introduced in the blog “ What's The Difference Between BI Analyst and Data Scientist ?”, the Data Science Engagement methodology in Figure 1 supports the rapid exploration, rapid testing, and continuous learning Data Science “Scientific Method [1] ”. Figure   1 :    Data Science ...
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