Topics: 
Nov
2017 Dec Opinions, Interviews
All (93) | Courses, Education (2) | Meetings (8) | News, Features (10) | Opinions, Interviews (35) | Top Stories, Tweets (9) | Tutorials, Overviews (23) | Webcasts & Webinars (6)
- Lessons from Game of Thrones: Stopping the White Walkers of Data Monetization
- Dec 29, 2017.
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!
- Data Science for Laymen: 5 Writers Who Speak Your Language
- Dec 28, 2017.
Here are 5 excellent Data Scientists who are also very good at explaining concepts and interacting with you.
- How AI Learns What You’re Willing to Pay
- Dec 28, 2017.
Why are we all paying different prices? Is it price "personalization" or price "discrimination"? The answer isn't so simple.
- Machine Learning Engineer, Data Scientist – top US emerging jobs
- Dec 27, 2017.
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.
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Can I Become a Data Scientist: Research into 1,001 Data Scientist Profiles
- Dec 26, 2017.
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. - View from Google Assistant: Are we becoming reliant on AI?
- Dec 26, 2017.
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.
- Demystifying Data Science
- Dec 26, 2017.
Marketing scientist Kevin Gray asks Dr. Randy Bartlett of Blue Sigma Analytics what Data Science really is and how it can help decision-makers.
- Yet Another Day in the Life of a Data Scientist
- Dec 25, 2017.
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.
- Is Religion The Next Frontier For AI?
- Dec 23, 2017.
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?
- DeepSchool.io: Deep Learning Learning
- Dec 22, 2017.
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.
- How to Improve Machine Learning Algorithms? Lessons from Andrew Ng, part 2
- Dec 21, 2017.
The second chapter of ML lessons from Ng’s experience. This one will only be talking about Human Level Performance & Avoidable Bias.
- Why Use Data Analytics to Prevent, Not Just Report
- Dec 21, 2017.
The best way to reduce operating and business costs and risks is to prevent them!
- 70 Amazing Free Data Sources You Should Know
- Dec 20, 2017.
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.
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How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?
- Dec 20, 2017.
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. - Industry Predictions: Main AI, Big Data, Data Science Developments in 2017 and Trends for 2018
- Dec 19, 2017.
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?
- Accelerating Algorithms: Considerations in Design, Algorithm Choice and Implementation
- Dec 18, 2017.
If you are trying to make your algorithms run faster, you may want to consider reviewing some important points on design and implementation.
- Building an Audio Classifier using Deep Neural Networks
- Dec 15, 2017.
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.
- Transitioning to Data Science: How to become a data scientist, and how to create a data science team
- Dec 15, 2017.
"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."
- Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018
- Dec 15, 2017.
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.
- How Big Data and New Technologies Are Changing Aging
- Dec 14, 2017.
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.
- Xavier Amatriain’s Machine Learning and Artificial Intelligence Year-end Roundup
- Dec 14, 2017.
So much has happened in the world of AI that it is hard to fit in a couple of paragraphs. Here is my attempt.
- How to Improve Machine Learning Performance? Lessons from Andrew Ng
- Dec 13, 2017.
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.
- Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018
- Dec 12, 2017.
The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.
- TensorFlow for Short-Term Stocks Prediction
- Dec 12, 2017.
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.
- Creating Simple Data Visualizations as an Act of Kindness
- Dec 12, 2017.
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.
- No More Excuses – 470 Outstanding Women in Analytics
- Dec 12, 2017.
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.
- Top Data Science and Machine Learning Methods Used in 2017
- Dec 11, 2017.
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".
- Robust Algorithms for Machine Learning
- Dec 11, 2017.
This post mentions some of the advantages of implementing robust, non-parametric methods into our Machine Learning frameworks and models.
- Another Day in the Life of a Data Scientist
- Dec 11, 2017.
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.
- 5 Tricks When A/B Testing Is Off The Table
- Dec 8, 2017.
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.
- Bill Inmon on Hearing The Voice Of Your Customer
- Dec 7, 2017.
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.
- When reinforcement learning should not be used?
- Dec 6, 2017.
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.
- Exclusive: Interview with Rich Sutton, the Father of Reinforcement Learning
- Dec 5, 2017.
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.
- 4 Common Data Fallacies That You Need To Know
- Dec 5, 2017.
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.
- Using Deep Learning to Solve Real World Problems
- Dec 4, 2017.
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.
