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Articles related to "problem"


Data Science’s Most Misunderstood Hero

  • The top trophy hire in data science is elusive, and it’s no surprise: “full-stack” data scientist means mastery of machine learning, statistics, and analytics.
  • A frequent lament among business leaders is, “Our data science group is useless.” and the problem usually lies in an absence of analytics expertise.
  • Statisticians and machine learning engineers are narrow-and-deep (the shape of a rabbit hole, incidentally) workers, so it’s really important to point them at problems that deserve the effort.
  • Where statisticians and ML folk are slow, analysts are a whirlwind of inspiration for decision-makers and other data science colleagues.
  • Good analysts have unwavering respect for the one golden rule of their profession: do not come to conclusions beyond the data.
  • Because subject matter expertise goes a long way towards helping you spot interesting patterns in your data faster, the best analysts are serious about familiarizing themselves with the domain.

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Math Vs Coding: Data Science

  • If I know the entire math logic for machine learning algorithms, but I could not code well, do I stand a chance to enter the data science field?
  • If I just barely know the math behind those machine learning algorithms, but I can code well, am I qualified enough to be a data scientist?
  • Thus, having knowledge in different programming languages is definitely a plus so that you could save your time on writing the whole code in Python in order to train the model.
  • Mathematics and coding are equally important in data science, but if you are considering to switch or start your career in the data science field, I would say coding or programming skills are more important than deep dive to the math for various kinds of machine learning models.
  • His experiences involved more on crawling websites, creating data pipeline and also implementing machine learning models on solving business problems.

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Data Science’s Most Misunderstood Hero

  • The top trophy hire in data science is elusive, and it’s no surprise: “full-stack” data scientist means mastery of machine learning, statistics, and analytics.
  • A frequent lament among business leaders is, “Our data science group is useless.” and the problem usually lies in an absence of analytics expertise.
  • Statisticians and machine learning engineers are narrow-and-deep (the shape of a rabbit hole, incidentally) workers, so it’s really important to point them at problems that deserve the effort.
  • Where statisticians and ML folk are slow, analysts are a whirlwind of inspiration for decision-makers and other data science colleagues.
  • Good analysts have unwavering respect for the one golden rule of their profession: do not come to conclusions beyond the data (and prevent your audience from doing it too).
  • Because subject matter expertise goes a long way towards helping you spot interesting patterns in your data faster, the best analysts are serious about familiarizing themselves with the domain.

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20 Most-Recommended Books for Software Developers

  • I've compiled the suggestions of dozens of programmers, managers, career coaches, and other industry professionals to bring you a list of the 20 most-recommended books for software developers, with some short summaries (courtesy of Amazon).
  • This is -- as far as I can tell -- the most complete meta-list of software development book recommendations anywhere on the Internet.
  • If you find a list of book recommendations for general software development which isn't included in my sources spreadsheet on GitHub, please let me know.
  • The author includes OOD, UML, Design Patterns, Agile and XP methods with a detailed description of a complete software design for reusable programs in C++ and Java.
  • Using a practical, problem-solving approach, it shows how to develop an object-oriented application—from the early stages of analysis, through the low-level design and into the implementation.

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Data Science’s Most Misunderstood Hero

  • The top trophy hire in data science is elusive, and it’s no surprise: “full-stack” data scientist means mastery of machine learning, statistics, and analytics.
  • A frequent lament among business leaders is, “Our data science group is useless.” and the problem usually lies in an absence of analytics expertise.
  • Statisticians and machine learning engineers are narrow-and-deep (the shape of a rabbit hole, incidentally) workers, so it’s really important to point them at problems that deserve the effort.
  • Where statisticians and ML folk are slow, analysts are a whirlwind of inspiration for decision-makers and other data science colleagues.
  • Good analysts have unwavering respect for the one golden rule of their profession: do not come to conclusions beyond the data (and prevent your audience from doing it too).
  • Because subject matter expertise goes a long way towards helping you spot interesting patterns in your data faster, the best analysts are serious about familiarizing themselves with the domain.

save | comments | report | share on


Data Science’s Most Misunderstood Hero

  • The top trophy hire in data science is elusive, and it’s no surprise: “full-stack” data scientist means mastery of machine learning, statistics, and analytics.
  • A frequent lament among business leaders is, “Our data science group is useless.” and the problem usually lies in an absence of analytics expertise.
  • Statisticians and machine learning engineers are narrow-and-deep (the shape of a rabbit hole, incidentally) workers, so it’s really important to point them at problems that deserve the effort.
  • Where statisticians and ML folk are slow, analysts are a whirlwind of inspiration for decision-makers and other data science colleagues.
  • Good analysts have unwavering respect for the one golden rule of their profession: do not come to conclusions beyond the data.
  • Because subject matter expertise goes a long way towards helping you spot interesting patterns in your data faster, the best analysts are serious about familiarizing themselves with the domain.

save | comments | report | share on


#discuss20 Most-Recommended Books for Software Developers

  • I've compiled the suggestions of dozens of programmers, managers, career coaches, and other industry professionals to bring you a list of the 20 most-recommended books for software developers, with some short summaries (courtesy of Amazon).
  • This is -- as far as I can tell -- the most complete meta-list of software development book recommendations anywhere on the Internet.
  • If you find a list of book recommendations for general software development which isn't included in my sources spreadsheet on GitHub, please let me know.
  • The author includes OOD, UML, Design Patterns, Agile and XP methods with a detailed description of a complete software design for reusable programs in C++ and Java.
  • Using a practical, problem-solving approach, it shows how to develop an object-oriented application—from the early stages of analysis, through the low-level design and into the implementation.

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Forward and Backpropagation in GRUs — Derived | Deep Learning

  • In this article, we first take a brief overview of GRU networks, following which we will do a detailed mathematical derivation of the backpropagation equations using a computation graph.
  • Gated Recurrent Units uses the update and reset gates to tackle the gradient vanishing problem faced in RNNs. In the above image, at each time t, we have the state h and the current time input x.
  • For example, in the problem where we use time series weather data to predict the future weather, we might have some feature in the input like the population of the city, which the network might learn to be irrelevant to the weather prediction and “reset”.The update gate learns what data in the state to update with newer data from the input.

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Careers - Scale

  • One of the biggest bottlenecks for real world AI applications is access to labeled data.
  • Our first product is the most developer-friendly data labeling API for AI applications: self-driving cars, mapping, AR/VR, robotics, drones, retail, and more.
  • Our products for image annotation, semantic segmentation, 3D point cloud annotation, and LIDAR and RADAR annotation are used by industry leaders and provide world-class accuracy.
  • Coming out of YC S16, we have been able to partner with industry leaders to accelerate their AI development.
  • Scale is the standard solution for quality, cost, and scalability and takes the pain out of annotating data and creating high quality datasets.
  • Our view is that the most impactful way for us to change the future 10 years from now is by fundamentally bending the growth curve of machine learning software.
  • Enjoy time to travel or plan a staycation - whatever you need to relax and recharge.

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7 Great Programming Quotes

  • Here are some amazing quotes about software development to inspire you, make you laugh, or make you think.
  • Especially important for beginners.
  • At first we're so anxious about knowing everything, especially language syntax.
  • Problem solving is the skill we end up using most.
  • Programmers are mostly "learn by doing" types.
  • No amount of academic study or watching other people code can compare to breaking open an editor and start making mistakes.
  • When managing developers I would always encourage getting up and walking away from the computer when you have a problem.
  • Some of your best solutions will come to you when you're not at the machine.
  • Programmers don't.
  • Some developers hate testing.
  • However shifting your attitude and embracing it makes you a better developer.
  • To be a programmer long term, you have to love change.
  • You can't just tolerate it, you have to love it.

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