Dec2019 Jan Opinions
All (55) | Courses, Education (6) | Meetings (8) | News (5) | Opinions (14) | Top Stories, Tweets (6) | Tutorials, Overviews (13) | Webcasts & Webinars (3)
- 10 Exciting Ideas of 2018 in NLP
- Jan 16, 2019.
We outline a selection of exciting developments in NLP from the last year, and include useful recent papers and images to help further assist with your learning.
- How to go from Zero to Employment in Data Science
- Jan 15, 2019.
We propose the quickest and surest way to go from zero experience to landing a job, either in data science generally, or specifically in a new programming language or a new technology.
- The 6 Most Useful Machine Learning Projects of 2018
- Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
- Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated
- Jan 14, 2019.
Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
- Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data
- Jan 12, 2019.
A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.
- The year in AI/Machine Learning advances: Xavier Amatriain 2018 Roundup
- Jan 11, 2019.
A summary of the main machine learning advances from 2018, including AI hype cooling down, interpretability, deep learning, NLP, and more.
- Explainable Artificial Intelligence
- Jan 10, 2019.
We outline the necessity of explainable AI, discuss some of the methods in academia, take a look at explainability vs accuracy, investigate use cases, and more.
- The Role of the Data Engineer is Changing
- Jan 10, 2019.
The role of the data engineer in a startup data team is changing rapidly. Are you thinking about it the right way?
- 4 Myths of Big Data and 4 Ways to Improve with Deep Data
- Jan 9, 2019.
There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.
- Core Principles of Sustainable Data Science, Machine Learning and AI Product Development: Research as a core driver
- Jan 9, 2019.
Regardless of the size of your organisation, if you are developing machine learning or AI products, the core asset you have is a research professional, data scientist or AI scientist, regardless of their academic background.
- A Non-Compromising Approach to Privacy-Preserving Personalized Services
- Jan 8, 2019.
Could one even achieve both high privacy and high utility? Yes, and we explain how.
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The Five Best Data Visualization Libraries
- Jan 7, 2019.
There are plenty of library options out there to make great visualizations. We outline five of the best, complete with code examples and explanations, that will enable you to create and build interactive visualizations. -
The cold start problem: how to build your machine learning portfolio
- Jan 4, 2019.
This post outlines what makes a good machine leaning portfolio, with useful examples to help you begin to understand the type of project that gets noticed by big companies. - Ensemble Learning: 5 Main Approaches
- Jan 3, 2019.
We outline the most popular Ensemble methods including bagging, boosting, stacking, and more.