This one picture shows what areas of calculus and linear algebra are most useful for data scientists.
If you read any article worth its salt on the topic Math Needed for Data Science, you'll see calculus mentioned. Calculus (and it's closely related counterpart, linear algebra) has some very narrow (but very useful) applications to data science. If you have a decent algebra background (which I'm assuming you do, if you're a data scientist!) then you can learn…Continue
I had this conversation some time ago with an Engineer who came from a traditional background.
By that I mean, he had been in the same industry (heavy engineering) for 30 years.
Of these, he had been in the same company for 25 years (and this was his second job).
After understanding from me about data science, he said that as an engineer, he did…Continue
Added by ajit jaokar on July 30, 2020 at 3:00am — No Comments
Summary: An update and observations about China’s plan to become the world leader in AI.
Whether you get your news from Facebook or from the Wall Street Journal you can’t help having heard that China is out to displace the US as the world leader in AI. Variously you may have heard that…Continue
Added by William Vorhies on July 28, 2020 at 8:32am — No Comments
"If you want to learn something well, explain it."
What is TensorFlow?
Let's look it up on the TensorFlow.org page: "It's an end-to-end open-source machine learning platform for everyone." When TensorFlow version 2.0 came out in 2019, it was praised as a significant step towards the democratization of AI: "Now anyone can get their hands on the steering wheel!" When we sat down together to draft this article…Continue
Added by Rafael Knuth on July 28, 2020 at 8:30am — No Comments
Dropout means to drop out units which are covered up and noticeable in a neural network. Dropout is a staggeringly in vogue method to overcome overfitting in neural networks.
Deep Learning framework is now getting further and more profound. With these bigger networks, we can accomplish better prediction exactness. However, this was not the case a few years ago. Deep…Continue
Added by saurav singla on July 28, 2020 at 12:12am — No Comments
Added by Vincent Granville on July 26, 2020 at 5:00pm — No Comments
P-values and critical values are so similar that they are often confused. They both do the same thing: enable you to support or reject the null hypothesis in a test. But they differ in how you get to make that decision. In other words, they are two different approaches to the same result. This picture sums up the p value vs critical value approaches.…Continue
Added by Stephanie Glen on July 26, 2020 at 7:42am — No Comments
IBM Research, with the help of the University of Texas Austin and the University of Maryland, has tried to expedite the performance of neural networks by creating technology, called BlockDrop. Behind the design of this technology lies the objective and promise of speeding up convolutional neural network operations without…Continue
Added by Sharmistha Chatterjee on July 26, 2020 at 7:30am — No Comments
Use of SecureSVM, Boosting, Bagging, Clustering, LSTM, CNN, GAN in Retail with BlockChain
This blog highlights different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. This blog is structured as follows:
Added by Sharmistha Chatterjee on July 26, 2020 at 7:22am — No Comments
Added by Sharmistha Chatterjee on July 26, 2020 at 7:14am — No Comments
There are five types of traditional time series models most commonly used in epidemic time series forecasting, which includes
AR models express the current value of the time series linearly in…Continue
Added by Sharmistha Chatterjee on July 26, 2020 at 7:09am — No Comments
Traditional vs Deep Learning Algorithms in the Telecom Industry — Cloud Architecture and Algorithm Categorization
Google Cloud Architecture for Machine Learning Algorithms in the Telecom Industry
The unprecedented growth of mobile devices, applications, and services have placed the utmost demand on mobile and wireless networking infrastructure. Rapid research…Continue
Added by Sharmistha Chatterjee on July 23, 2020 at 8:26pm — No Comments
I can't find anymore where this chart, featuring relations between distributions, was first published. I remember seeing it on the Cloudera blog.
Another shorter one featuring the most useful one for statistical analysis, can be found…Continue
In the era of the Internet, the ability to crunch large amounts of data and process it with speed and efficiency has become essential for businesses to survive. But you try sitting down and going through all that data by hand: you'll get done sometime in the year 2050 if you're lucky.
That's where machine learning comes to the rescue. But…Continue
Added by Or Hillel on July 22, 2020 at 9:59pm — No Comments
Earlier this week, I was speaking at an event on AI for Real Estate where I showed an example from a BBC clip which said that “central London is now a ghost town” (due to COVID 19)
A few months ago, this headline would have been laughable
In London, central London and the London underground are a key fabric of daily…Continue
Added by ajit jaokar on July 22, 2020 at 2:00pm — No Comments
This post is about improving the effectiveness of the data science team and improving collaboration between data scientists and stakeholders for better outcomes.
Regardless of the specific project, agreeing on the expected outcomes and goals before beginning the work is a best practice. But with the advent of machine learning (ML) models, it’s for both sides to discuss the critical measures of success for the…Continue
Added by Ram Alagianambi on July 20, 2020 at 9:00pm — No Comments
As a data scientist, have you ever been frustrated that your stakeholders don’t see the value that you bring to the table? You may ask yourself, “How far should I go in explaining the work I do or what my models are doing?” If that sounds like you, then pay close attention to this post and the next, as they are all about improving collaboration between data scientists and other stakeholders.
This is a two-part post: This article…Continue
Added by Ram Alagianambi on July 20, 2020 at 8:30pm — No Comments
Summary: Advances in very low cost compute and Model Based Reinforcement Learning make this modeling technique that much closer to adoption in the practical world.
Added by William Vorhies on July 20, 2020 at 12:30pm — No Comments
Over the last couple of years, there has been a lot of hype around robotic process automation. This makes a lot of sense if you consider that in 2018 Gartner was already labeling it “…Continue
Added by Daniel Pullen on July 20, 2020 at 4:30am — No Comments
Added by Vincent Granville on July 19, 2020 at 4:00pm — No Comments