We are living in an age where every day about 500 million tweets are being sent out, every hour about 4 million photos are being uploaded to Instagram, every minute about 300 hours of video is being uploaded to YouTube
We are living in an age where every day about 500 million tweets are being sent out, every hour about 4 million photos are being uploaded to Instagram, every minute about 300 hours of video is being uploaded to YouTube, every second about 750,000 messages are being sent in WhatsApp, every millisecond about 2900 emails are being sent and the list goes on. The amount of data collected per day by 2025 is estimated to be about 463 exabytes!. Developing machine learning or deep learning algorithms that consume such voluminous data is going to be a challenge for data scientists. At this point, it becomes quite imperative to develop these algorithms efficiently so that they can process the data at a faster rate (of course we need better hardware to run these algorithms, but let’s just leave that to NVIDIA folks!). And this is where parallel computing comes into picture. As the name says, it a type of computation in which execution of calculations or processes are done simultaneously, resulting in a significant boost in the performance of the algorithm. Multiprocessing and Threading are two inbuilt modules in Python that allows us to perform parallel computing. In this article, we will explore how data scientists can make use of these modules to speed up their pipelines. Towards the end of this article, we will apply these techniques to two well-known areas in deep learning namely, Computer Vision and Natural Language Processing and see the benefits of parallel computing.
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In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python.
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