As I watched the impending battle between the White Walkers and humanity, I couldn’t help but identify a number of lessons that we can learn from Jon Snow’s battle with the leader of the White Walkers… and the power of Valyrian steel!
Also: Ten years in, nobody has come up with a use for #blockchain - here is what happened; Can I Become a #DataScientist: Research into 1,001 #DataScience Profiles.
Machine Learning Engineer jobs grew almost 10 fold since 2012, and Data Scientist jobs grew 6.5 times. However, finding qualified people to fill such jobs remains difficult.
In this SQL window functions tutorial, we will describe how these functions work in general, what is behind their syntax, and show how to answer these questions with pure SQL.
Results from a survey include: the average data scientist is a male, with median experience on the job is 2 years. He uses R, Python, and SQL. Read for more details.
AI is powering a paradigm shift in human machine interaction and conversational UIs like Alexa, Cortana, Google Assistant, and Siri, have the potential to break free from some key limitations of mobile app.
Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these four individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
Different civilizations have worshiped many different gods and deities. Science, discovery and new technologies have influenced religion in the past, so will our digital age should birth an AI god?
A great explanation of the concept behind Monte Carlo Tree Search algorithm and a brief example of how it was used at the European Space Agency for planning interplanetary flights.
What I truly envision for deep school is that this will build a whole lot of Meetup nodes across the world where people will learn, mentor and network around sharing AI knowledge.
So normally we do Deep Learning programming, and learning new APIs, some harder than others, some are really easy an expressive like Keras, but how about a visual API to create and deploy Deep Learning solutions with the click of a button? This is the promise of Deep Cognition.
The Art of Learning #DataScience; How to Generate FiveThirtyEight Graphs in #Python; #TensorFlow for Short-Term Stocks Prediction; 15 Mathematics MOOCs for #DataScience.
Cutting-edge science and new business fundamentals intersect and merge at Strata Data Conference. Win KDnuggets Pass - submit your entry by Jan 3, 2018.
70 free data sources for 2017 on government, crime, health, financial and economic data, marketing and social media, journalism and media, real estate, company directory and review, and more to start working on your data projects.
When I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don’t know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics.
Check Packt $5 sale on every ebook and video, including many great titles on Data Analysis, Machine Learning, Python, Deep Learning, and more - sale runs until Jan 15, 2018.
Announcing Deep Learning World: The call-for-speakers for the inaugural Deep Learning World, June 3-7, 2018 in Las Vegas is open. Agenda now posted for Predictive Analytics World, Las Vegas – June 3-7, 2018.
Here is a treasure trove of analysis and predictions from 17 leading companies in AI, Big Data, Data Science, and Machine Learning: What happened in 2017 and what will 2018 bring?
Customer retention curves are essential to any business looking to understand its clients, and will go a long way towards explaining other things like sales figures or the impact of marketing initiatives. They are an easy way to visualize a key interaction between customers and the business.
Also: Another Day in the Life of a Data Scientist; The 10 Deep Learning Methods AI Practitioners Need to Apply; Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018; Top 10 Machine Learning Algorithms for Beginners
Third year Ph.D student David Abel, of Brown University, was in attendance at NIP 2017, and he labouriously compiled and formatted a fantastic 43-page set of notes for the rest of us. Get them here.
Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.
"A good data scientist in my mind is the person that takes the science part in data science very seriously; a person who is able to find problems and solve them using statistics, machine learning, and distributed computing."
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2017 and their 2018 key trend predictions.
CogX 2018 (11-12 June, London) will be the most important AI event in Europe. Get early bird tickets for only £599 (reduced from £1,799) with code KDN15 (valid December 2017).
NodeXL, the network overview discovery and exploration add-in for the familiar Microsoft Office Excel (TM) spreadsheet brings network functions within the reach of people who are more comfortable making pie charts than writing code. See what NodeXL finds in KDnuggets network and download NodeXL Pro for your analyses.
Holiday Dev & IT sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until Dec 21, 2017.
KDnuggets founder, Gregory Piatetsky-Shapiro, joins Michael Li, CEO and founder of The Data Incubator, Jan 11 at 2:30 pm PT/ 5:30 pm ET for their monthly webinar series, Data Science in 30 Minutes. Gregory will discuss his career - from Data Mining to Data Science and examine current trends in the field.
RE•WORK interview leading minds in the field to discuss the impact and progressions of AI on business and in society. The complimentary white paper 'Should You Be Using AI In Your Business?' is now available to download. Save 20% on globally renowned AI and Deep Learning summits with code KDNUGGETS.
Big data and new technologies are changing the healthcare industry and the aging process as we know it; and for now, that seems to be a move in the right direction.
In this post, we'll help you. Using Python's matplotlib and pandas, we'll see that it's rather easy to replicate the core parts of any FiveThirtyEight (FTE) visualization.
AI NEXTCon Seattle brings together top technical engineers, practitioners, influential technologists and data scientists to share solutions and practical experiences in machine/deep learning, computer vision, speech recognition and NLP.
Also The first international #beauty contest decided by #AI #algorithm sparked controversy; 4 Common #Data Fallacies That You Need To Know; Using #DeepLearning to Solve Real World Problems; Best Online Masters in #DataScience and #Analytics.
Here is a selection of some of the highest rated ODSC talks of 2017 as voted by our attendees. Also check out our series of bi-weekly data science and AI webinars. Attend ODSC East 2018 in person and save 70% with code KDNUGGETS!
5 useful tips and lessons from Andrew Ng on how to improve your Machine Learning performance, including Orthogonalisation, Single Number Evaluation Metric, and Satisfying and Optimizing Metric.
Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.
In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.
The field of data visualization is still quite young and evolving rapidly—and tools like the web and VR are continuing to expand the possibilities. So there is a lot of room for exploring new possibilities and creating new formats, as well as many examples of novel and amazing visualizations.
In case your network doesn’t include many of the remarkable women you might consider, I have some lists to get you started. Here’s where to find more information and links to profiles of 470 of the industry’s best.
The most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests; Deep Learning is used by only 20% of respondents; we also analyze which methods are most "industrial" and most "academic".
AnacondaCON is coming to Austin, TX April 8-11. Register now to take advantage of our Early Bird offer of two tickets for the price of one. We’re also offering a bonus 10% off your ticket price if you book a hotel room at the conference site.
Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
Also: What is a Bayesian Neural Network?; Today I Built a Neural Network During My Lunch Break with Keras; 4 Common Data Fallacies That You Need To Know; Top 10 Machine Learning Algorithms for Beginners; The 10 Statistical Techniques Data Scientists Need to Master
Learn how Vertica in-database machine learning supports the entire predictive analytics process with, with MPP, SQL execution, R, Python, Java and more - get the whitepaper.
Sometimes you cannot do A/B testing, but it does not mean we have to fly blind - there is a range of econometric methods that can illuminate the causal relationships at play.
So yesterday someone told me you can build a (deep) neural network in 15 minutes in Keras. Of course, I didn’t believe that at all. So the next day I set out to play with Keras on my own data.
Also: Best Online Masters in Data Science and Analytics - a comprehensive, unbiased survey; Deep Learning Specialization by Andrew Ng - 21 Lessons Learned; Machine Learning Algorithms: Which One to Choose for Your Problem; Want to know how Deep Learning works? Here's a quick guide
Get real performance results and download the free Intel® Distribution for Python that includes everything you need for blazing-fast computing, analytics, machine learning, and more. Use Intel Python with existing code, and you’re all set for a significant performance boost.
This post explores the importance of hearing your customer, and how to use sentiment analytics and other technologies to achieve this goal and avoid going out of business.
Also An Introduction to Key Data Science Concepts; Using Deep Learning To Extract Knowledge From Job Descriptions; A General Approach to Preprocessing Text Data; keras-text - A Text Classification Library in #Keras.
We take a quick look at how web scraping can be useful in the context of data science projects, eg to construct a social graph based of S&P 500 companies, using Python and Gephi.
While reinforcement learning has achieved many successes, there are situations when it use is problematic. We describe the issues and how to work around them.
Only the Godfather of Deep Learning did it again and came up with something brilliant — adding layers inside existing layers instead of adding more layers i.e nested layers.... giving rise to the Capsule Networks!
Attend the AI World Expo and meet with 50+ exhibitors and AI Startups. Save $200 off your 2 or 3-day VIP conference pass using priority code AIW200KD. Save $100 off your AI World Expo Pass using priority code AIW100EXPD. Priority codes expire Dec 7.
Join DataRobot, Dec 13, for a webinar discussion of the current state of machine learning in fraud detection and learn how you can stay one step ahead of those looking to harm your business.
By having the model analyze the important signals, we can focus on the right set of attributes for optimization. As a side effect, less attributes also mean that you can train your models faster, making them less complex and easier to understand.
My exclusive interview with Rich Sutton, the Father of Reinforcement Learning, on RL, Machine Learning, Neuroscience, 2nd edition of his book, Deep Learning, Prediction Learning, AlphaGo, Artificial General Intelligence, and more.
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2017 and their 2018 key trend predictions.
In this post you will find a list of common the data fallacies that lead to incorrect conclusions and poor decision-making using data. Here you will find great resources and information so that you can always be reminded of these fallacies when you’re working with data.
How do you build a chatbot your customers will actually want to talk to? At CrowdFlower, we’ve seen the data and the projects that do just that. And we’d like to share what we’ve learned in this free eBook.
We need to create a sense of urgency around exploring and analyzing data. We also need to train and empower individuals to know how. This video covers the need for students to enter the workforce with analytics skills and why we need to give employees permission to fail.
Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.
Also: How To Unit Test Machine Learning Code; Evolutionary Algorithms for Feature Selection; A General Approach to Preprocessing Text Data; The 10 Statistical Techniques Data Scientists Need to Master; Deep Learning Specialization by Andrew Ng - 21 Lessons Learned
The need to be broadly knowledgeable and rapidly understand the existing database ecosystem is growing. Looker broken down and simplified the differentiators of the main database technologies into this series of four, 45-minute webinar sessions.
Understand best practices for optimizing the handoff from analytic team to IT across your business as a core competency, how to create scalable peak model performance, and more.
Coming soon: H2O World, Mountain View; NIPS 2017, Long Beach; IEEE Big Data Boston; IE8NY17, NYC; Strata, Singapore; Deep Learning Summit, San Francisco; TDWI Las Vegas and many more.
We explore recurrent neural networks, starting with the basics, using a motivating weather modeling problem, and implement and train an RNN in TensorFlow.
Recently we had a look at a framework for textual data science tasks in their totality. Now we focus on putting together a generalized approach to attacking text data preprocessing, regardless of the specific textual data science task you have in mind.
Can I Become a Data Scientist: Research into 1,001 Data Scientist Profiles
Computer Vision by Andrew Ng - 11 Lessons Learned
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