Regarding with Gradient Descent in Data Science
Hello Everyone, I am learning Data Science and My few interviews are scheduled next to next week on skype. Can anyone explain in-depth information about gradient descent in Data Science, As my research, the degree of change in the output of a function relating to the changes made to the inputs is known as a gradient. It measures the change in all weights with respect to the change in error. A gradient can also be comprehended as the slope of a function, this is according to this post https://hackr.io/blog/data-science-interview-questions when I was searching for this query. Can anyone know this is the perfect description of Gradient Descent?
Can anyone explain in-depth information about gradient descent in Data Science, As my research, - Any specific?
DataOps or DevOps? What is the difference?
Data is the key to success for many organisations and it should be gathered from and shared with all functions. This is what would happen in an ideal world.
DataOps utilizes the most effective practices of the Development Operations approach - Agree thanks for iinput
Big Data Testing Challenges
Enterprise data has grown 650% in the last five years, as a result about 85% of Fortune 500 organizations will be unable to exploit Big Data for competitive advantage. Data is the lifeline of an organization and is becoming more important each day. Big Data Testing is a trending topic in the Software Industry, its various properties like volume, velocity, variety, variability, value, complexity and performance creates many challenges.
Data is the lifeline of an organization and is becoming more important each day. - A point to note quite agree
Get Free Spark NLP for Healthcare Licenses to Fight COVID-19
John Snow Labs, the winner of the Data Science Technology award in the International Data Science Awards 2019, is making all its licensed software – Spark NLP for Healthcare, the Healthcare AI Platform, and the Curated Datasets – available for free to data scientists who are actively tackling COVID-19.
Get Free Spark NLP for Healthcare Licenses to Fight COVID-19,This is superb information. Thanks for this article. Nicely written.
Tool differences between data science and analytics?
Tool differences between data science and analytics? can anybody bring out the key difference between these two subject ?
Data Science simply means collection, combing out and reviewing the data collected by various tools and preparing it for the review. Data Analytics is where you analyse this data to get insights into meaningful co-relation between data available. Both of these aspects are caught in-depth at ITM’s 11 weeks long PGP.
How does R shiny works?
How does R shiny works? How can we integrate it with some dashboard and make it to productions? any tips any example to share?
The Shiny web framework is fundamentally about making it easy to wire up input values from a web page, making them easily available to you in R, and have the results of your R code be written as output values back out to the web page.
NLP and NLG how different are they?
NLP and NLG how different are they? can we bring out some key difference and way it works..some examples?
NLGOnce a chatbot, smart device, or search function understands the language it’s “hearing,” it has to talk back to you in a way that you, in turn, will understand. That’s where NLG comes in. It takes data from a search result, for example, and turns it into understandable language. So whenever you ask your smart device, “What’s it like on I-93 right now?” it can answer almost exactly as another human would. It may say something like, “There is an accident at exit 36 that has created a 15-minute delay,” or “The road is clear.” NLG is used in chatbot technology, as well. In fact, chatbots have become so advanced; you may not even know you’re talking to a machine. Using NLP, NLG, and machine learning in chatbots frees up resources and allows companies to offer 24/7 customer service without having to staff a large department.
Which of these measures are used to analyze the central tend
Which of these measures are used to analyze the central tendency of data?
Which is Best—the Mean, Median, or Mode? When you have a symmetrical distribution for continuous data, the mean, median, and mode are equal. In this case, analysts tend to use the mean because it includes all of the data in the calculations. However, if you have a skewed distribution, the median is often the best measure of central tendency. When you have ordinal data, the median or mode is usually the best choice. For categorical data, you have to use the mode.
Redundant Test Case
Redundancy has a negative connotation when the duplication is unnecessary or is simply the result of poor planning.Length of test run is what drives you to want to act on this data. But we are seeing things covered redundantly. That is nearly always going, but when you can couple it to a single redundant test, you have an opportunity to delete that test to reduce the elapsed time of a test suite.
What is the difference between static and dynamic testing?
Static testing: During Static testing method, the code is not executed, and it is performed using the software documentation.Dynamic testing: To perform this testing the code is required to be in an executable form.