Machine Learning Practitioners have different personalities. While some of them are “I am an expert in X and X…
Summary: Are you wondering about moving up to Automated Machine Learning (AML)? Here are some considerations to help guide you.
Are you wondering about moving up to Automated Machine…Continue
Added by William Vorhies on July 15, 2019 at 7:46am — No Comments
This post is a part of my forthcoming book on Mathematical foundations of Data Science.
In the previous blog, we saw how you could use basic high school maths to learn about the workings of data science and artificial intelligence
In this post we extend that idea to learn about Gradient descent
Added by ajit jaokar on July 14, 2019 at 12:30pm — No Comments
Summary: We’re rapidly approaching the point where AI will be so pervasive that it’s inevitable that someone will be injured or killed. If you thought this was covered by simple product defect warranties it’s not at all that clear. Here’s what we need to start thinking about.
Added by Vincent Granville on July 12, 2019 at 6:30am — No Comments
This glossary defines general machine learning terms as well as terms specific to TensorFlow. Below is a small selection of the most popular entries. You can access this glossary here. For other related glossaries, follow this link.
Added by Capri Granville on May 23, 2019 at 8:30am — No Comments
This article was written by James Loy.
Update: When I wrote this article a year ago, I did not expect it to be thispopular. Since then, this article has been viewed more than 450,000 times, with more than 30,000 claps. It has also made it to the front page of Google, and it is among the first few search results for…Continue
Added by Andrea Manero-Bastin on July 4, 2019 at 4:30am — No Comments
Enabling cost-effective terabyte main memory along with third-party observations of a “profound” change in storage, Intel® Optane™ DC persistent memory modules change the way we think about memory capacity, tiered memory, out-of-core algorithms, storage speed, and essentially what we can do on a computational “fat node” and with persistent storage.
With a growing ecosystem of more than 50 OEMs, ISVs and cloud service providers are exploring what is possible given the touted three-fold…Continue
Added by Rob Farber on July 8, 2019 at 1:51pm — No Comments
The course "Data Science for Business Innovation" is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation.
It consists of introductory lectures spanning big data, machine learning, data valorization and communication for a non-technical audience.
You can register for free on Coursera at this…Continue
Added by Marco Brambilla on July 9, 2019 at 4:30am — No Comments
Added by Shaily Baheti on July 9, 2019 at 11:00pm — No Comments
Recently we have discussed the emerging concept of smart farming that makes agriculture more efficient and…Continue
Added by Max Ved on July 10, 2019 at 8:30am — No Comments
A key strength of NLP (natural language processing) is being able to process large amounts of texts and then summarise them to extract meaningful insights.
In this example, a selection of economic bulletins in PDF format from 2018 to 2019 are analysed in order to gauge economic sentiment. The bulletins in question are sourced from the European Central Bank website. tf-idf is used to rank…Continue
Added by Michael Grogan on July 11, 2019 at 12:28pm — No Comments
If you are a recent graduate or someone preparing for your first data scientist position, then here are some tips to help you ace your interview!…Continue
Added by Ann Rajaram on July 11, 2019 at 2:52pm — No Comments
Evaluating a model is just as important as creating the model in the first place. Even if you use the most statistically sound tools to create your model, the end result may not be what you expected. Which metric you use to test your model depends on the type of data you’re working with and your comfort level with statistics.
Model evaluation techniques answer three main questions:
SVMs (Support Vector Machines) are a way to classify data by finding the optimal plane or hyperplane that separates the data. In 2D, the separation is a plane; In higher dimensions, it's a hyperplane. For simplicity, the following picture shows how SVM works for a two-dimensional set.
Click on picture to zoom…Continue
I highly recommend the #StateofAI 2019 report. I have followed this report from By Nathan Benaich and Ian Hogarth
The report is free and you can download it at stateofai 2019
The report is kind of Mary Meeker theme for AI for me i.e. a great reference…Continue
Summary: More data means better models but we may be crossing over a line into what the public can tolerate, both in the types of data collected and our use of it. The public seems divided. Targeted advertising is good but the increased invasion of privacy is bad.
Headlines are full of alarm. The public is up in arms. The internet is stealing their privacy. Indeed, the Future of Humanity Institute at Oxford rates this as the most…Continue
Added by William Vorhies on July 8, 2019 at 7:30am — No Comments
I love watching the NBA’s Golden State Warriors play basketball. Their offensive “improvisation” is a thing of beauty in their constant ball movement in order to find the “best” shot. They are a well-oiled machine optimizing split-second decisions in an ever-changing landscape that is heavily influenced by questions such as:
Added by Bill Schmarzo on July 5, 2019 at 12:51pm — No Comments
The use of Docker in conjunction with AWS can be highly effective when it comes to building a data pipeline.
Let me ask you if you have ever had this situation before. You are building a model in Python which you need to send over to a third-party, e.g. a client, colleague, etc. However, the person on the other end cannot run the code! Maybe they don't have the right libraries installed, or their system is not configured correctly.
Whatever the reason, Docker alleviates this…Continue
Added by Michael Grogan on July 5, 2019 at 8:30am — No Comments
Co-Integration in Time Series Analysis is when one data points is depended on other data points or follow the pattern. Example in capital markets Industry or sector leader company stock leads the direction and many small companies follows it. Example : Crude oil and Gasoline prices. Price of gasoline is dependent on Crude oil prices. Here Crude oil price always drives gasoline prices.
To analyze similar co-integration used Moody's corporate AAA and BBB Bond Yields. Corporate…Continue
Added by Kali Prasad on July 6, 2019 at 10:37pm — No Comments
In my previous post, I discussed the relationship between role conflict and performance. I suggested that all things being equal, role conflict might be the primary determinant of employee performance. Companies direct all sorts of resources gathering data for recruitment purposes. All things being about the same, much of that data collection is irrelevant. …Continue
Added by Don Philip Faithful on July 7, 2019 at 6:11am — No Comments