Sep2017 Oct Opinions, Interviews
All (77) | Courses, Education (6) | Meetings (12) | News, Features (15) | Opinions, Interviews (20) | Tutorials, Overviews (20) | Webcasts & Webinars (4)
- 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.