Topics: 
Sep
2017 Oct Opinions, Interviews
All (117) | Courses, Education (7) | Meetings (18) | News, Features (18) | Opinions, Interviews (29) | Top Stories, Tweets (10) | Tutorials, Overviews (29) | Webcasts & Webinars (6)
- Top 6 errors novice machine learning engineers make
- Oct 30, 2017.
What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.
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AlphaGo Zero: The Most Significant Research Advance in AI
- Oct 27, 2017.
The previous version of AlphaGo beat the human world champion in 2016. The new AlphaGo Zero beat the previous version by 100 games to 0, and learned Go completely on its own. We examine what this means for AI. - Actionable Insights: Obliterating BI, Data Warehousing as We Know It
- Oct 27, 2017.
There is a big demand of quick insights or real time analytics from business side. But traditional BI or data warehouse architectures lack this realtime functionality. Here we discuss realtime analytics architecture in details.
- The danger in comparing your campaign performance against an average
- Oct 26, 2017.
Performance measurement is only meaningful when compared against a benchmark. While “average” is a good, and easy to understand metric, it could be very deceptive.
- Artificial Intelligence Today: Time to Act
- Oct 25, 2017.
The AI and advanced analytics conversation has risen all the way to C-suite. The time has come to act. Jump on the AI train soon or you will be left behind.
- Business intuition in data science
- Oct 24, 2017.
Data Science projects are not just use of algorithms & building models; there are other steps of the project which are equally important. Here we explain them in detail.
- How Can Machine Learning Affect Your Organizational Data Strategy?
- Oct 24, 2017.
The rise of high information advances, for example, Big Data, Machine Learning (ML), and the Internet of Things (IoT) in the Data Management scene has now started another enthusiasm for Data Governance.
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Ranking Popular Deep Learning Libraries for Data Science
- Oct 23, 2017.
We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results. - Rethinking 3 Laws of Machine Learning
- Oct 23, 2017.
We rethink Asimov’s 3 law of robotics to help companies moving to unsupervised machine learning and realize 100% automated predictive information governance (PIG).
- The ways that AI can change your business
- Oct 20, 2017.
AI technology involves a change in the value chain and represents a major challenge and opportunity for businesses. Managers are directly involved in this challenge, by accompanying the teams through this transition: vanquish fears, embracing innovation, transforming businesses, training teams.
- It Only Takes One Line of Code to Run Regression
- Oct 19, 2017.
I learned how important to understand data before running algorithms, how important it is to know the context and the industry before jumping on getting insights, how it is very easy to make models but tough to get them to work for you, and finally, how it only takes one line of code to run linear regression on your dataset.
- Learning git is not enough: becoming a data scientist after a science PhD
- Oct 18, 2017.
Here is useful advice about moving from academia into data science after completing a PhD in a natural science.
- Key Trends and Takeaways from RE•WORK Deep Learning Summit Montreal – Part 2: The Pioneers
- Oct 18, 2017.
The most anticipated aspect of the RE•WORK Deep Learning Summit Montreal was the assembly of deep learning pioneers Yoshua Bengio, Yann LeCun, and Geoff Hinton on stage separately and together for the first time at such an event.
- 4 Major Trends Influencing the 2017 Predictive Analytics Hiring Market
- Oct 17, 2017.
We examine the implications of trends in hiring market, including the growth of quantitative Initiatives, blurring of the lines between Predictive Analytics and Data Science Professionals, and more .
- RE•WORK Deep Learning Summit Montreal Panel of Pioneers Interview: Yoshua Bengio, Yann LeCun, Geoffrey Hinton
- Oct 17, 2017.
At the Deep Learning Summit in Montreal last week, we saw Yoshua Bengio, Yann LeCun and Geoffrey Hinton come together to share their most cutting edge research progressions as well as discussing the landscape of AI and the deep learning ecosystem in Canada.
- Social Media and Machine Learning Transform Self-service Data Prep
- Oct 16, 2017.
Social media and machine learning concepts are transforming self-service data prep into a collaborative data marketplace.
- Key Trends and Takeaways from RE•WORK Deep Learning Summit Montreal – Part 1: Computer Vision
- Oct 16, 2017.
Read up on what you missed from the RE•WORK Deep Learning Summit Montreal, held October 10 & 11, including talks from Aaron Courville, Ira Kemelmacher-Shlizerman, Roland Memisevic, and Raquel Urtasun.
- An Overview of 3 Popular Courses on Deep Learning
- Oct 13, 2017.
After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera (which is not completely released) and Udacity, I believe a post about what you can expect from these 3 courses will be useful for future Deep learning enthusiasts.
- Want to Become a Data Scientist? Read This Interview First
- Oct 13, 2017.
There’s been a lot of hype about Data Science... and probably just as much confusion about it.
- Strata Data Conference, NYC – Key Trends and Highlights
- Oct 12, 2017.
Strata is a conference I very much enjoyed attending. This year, I observed a few common themes that ran across much of the conference content: Data Science Collaboration, Data Ethics, and Platform Optimization.
- How I started with learning AI in the last 2 months
- Oct 9, 2017.
The relevance of a full stack developer will not be enough in the changing scenario of things. In the next two years, full stack will not be full stack without AI skills.
- Credible Sources of Accurate Information About AI
- Oct 9, 2017.
I want to recommend several credible sources of accurate information. Most of the writing on this list is intended to be accessible to anyone—even if you aren’t a programmer or don’t work in tech.
- 5 overriding factors for the successful implementation of AI
- Oct 6, 2017.
Today AI is everywhere, from virtual assistants scheduling meetings, to facial recognition software and increasingly autonomous cars. We review 5 main factors for the successful AI implementation.
- Data Science –The need for a Systems Engineering approach
- Oct 5, 2017.
We need a greater emphasis on the Systems Engineering aspects of Data Science. I am exploring these ideas as part of my course "Data Science for Internet of Things" at the University of Oxford.
- Find Out What Celebrities Tweet About the Most
- Oct 5, 2017.
Word cloud is a popular data visualisation method. Here we show how to use R to create twitter word cloud of celebrities and politicians.
- The 5 Best Industries to Find a Job in Data Science
- Oct 3, 2017.
There’s never been a better time to pursue a career in this field. With that in mind, here are five extremely practical and exciting fields you could leave a mark on with an education in data science.
- Statistical Mistakes Even Scientists Make
- Oct 3, 2017.
Scientists are all experts in statistics, right? Wrong.
- GPU-accelerated, In-database Analytics for Operationalizing AI
- Oct 2, 2017.
This blog explores how the massive parallel processing power of the GPU is able to unify the entire AI pipeline on a single platform, and how this is both necessary and sufficient for overcoming the challenges to operationalizing AI.
- Key Takeaways from AI Conference in San Francisco 2017 – Day 2
- Oct 2, 2017.
Highlights and key takeaways from day 2 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.
