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!
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.
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?
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.
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.
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?
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.
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.
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.
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".
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.
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.
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.
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.
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.
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.
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.
Can I Become a Data Scientist: Research into 1,001 Data Scientist Profiles
How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?
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