Jan
2019 Feb Opinions
All (102) | Courses, Education (5) | Meetings (18) | News (14) | Opinions (30) | Top Stories, Tweets (9) | Tutorials, Overviews (22) | Webcasts & Webinars (4)
- Preparing for the Unexpected - Feb 28, 2019.
In some domains, new values appear all the time, so it's crucial to handle them in a good way. Using deep learning, one can learn a special Out-of-Vocabulary embedding for these new values. But how can you train this embedding to generalize well to any unseen value? We explain one of the methods employed at Taboola.
- Top 7 Data Science Use Cases in Travel - Feb 28, 2019.
To satisfy all the needs of the growing number of consumers and process enormous data chunks, data science algorithms are vital. Let’s consider several of widespread and efficient data science use cases in the travel industry.
- Acquiring Labeled Data to Train Your Models at Low Costs - Feb 27, 2019.
We discuss groundbreaking and unique methods to acquire labeled data at low cost, including 3rd-Party Plug-and-Play AI Model, Zero-Shot Learning, and Restructuring the Existing Data Set.
- Reflections on the State of AI: 2018 - Feb 26, 2019.
We provide a detailed overview of the key developments in the AI space, focusing on key players, applications, opportunities, and challenges.
-
4 Reasons Why Your Machine Learning Code is Probably Bad - Feb 26, 2019.
Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG. - Asking Great Questions as a Data Scientist - Feb 25, 2019.
We outline the importance of asking yourself the questions you need to ask to effectively produce something that the business wants. Once you start asking questions, it’ll become second nature and you’ll immediately see the value and find yourself asking even more questions as you gain more experience.
- Where did you apply Analytics, Data Science, Machine Learning in 2018? - Feb 25, 2019.
Where did you apply Analytics, Machine Learning, and Data Science in 2018? Take part in the latest KDnuggets poll to share your input, and see what others have to say.
- Two Major Difficulties in AI and One Applied Solution - Feb 22, 2019.
Some of AI’s biggest problems can be solved by focusing on modelling our own human abilities instead of admiring NN and ML “intelligence”. We present an example that takes us in that direction in the form of chess.
- Word Embeddings in NLP and its Applications - Feb 20, 2019.
Word embeddings such as Word2Vec is a key AI method that bridges the human understanding of language to that of a machine and is essential to solving many NLP problems. Here we discuss applications of Word2Vec to Survey responses, comment analysis, recommendation engines, and more.
- 6 Books About Open Data Every Data Scientist Should Read - Feb 20, 2019.
Check out this collection of six books which tackle the hard skills required to make sense of the changing field known as open data and muse on the ethical implications of a digitally connected world.
- Automatic Machine Learning is broken - Feb 19, 2019.
We take a look at the arguments against implementing a machine learning solution, and the occasions when the problems faced are not ML problems and can perhaps be solved using optimization, exploratory data analysis tasks or problems that can be solved with simple statistics.
- Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019 - Feb 18, 2019.
We recap some of the major highlights in data science and AI throughout 2018, before looking at the some of the potential newest trends and technological advances for the year ahead.
- The Persuasion Paradox – How Computers Optimize their Influence on You - Feb 16, 2019.
How do computers optimize mass persuasion – for marketing, presidential campaigns, and even healthcare? And why is there actually no data that directly records influence, considering it's so important? In this season finale episode, Eric Siegel introduces machine learning methods designed to persuade.
- Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.
- Learn How to Listen: One of the hardest parts of being a data scientist - Feb 15, 2019.
Listen, Be Humble, Be Present and Transform ideas. A Data Scientist will spend a large amount of their time in meetings where you can understand the business, the goals of the area, their KPIs, and their requirements.
- Top 10 Data Science Use Cases in Telecom - Feb 14, 2019.
In this article, we attempt to present the most relevant and efficient data science use cases in the field of telecommunication.
- The Analytics Engineer – new role in the data team - Feb 13, 2019.
In a constantly changing landscape and with many companies, the roles and responsibilities of data engineers, analysts, and data scientists are changing, forcing the introduction of a new role: The Analytics Engineer.
- How AI can help solve some of humanity’s greatest challenges – and why we might fail - Feb 12, 2019.
AI represents a step change in humanity’s ability to rise to its greatest challenges. We explore three areas in which AI can contribute to the UN’s Global Goals - and why we could fall short.
- Natural Language Processing for Social Media - Feb 12, 2019.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Natural Language Processing and how it is used in social media analytics.
-
Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks. - Data Science For Our Mental Development - Feb 11, 2019.
In this blog, I aim to generalize how AI can help us with mental development in the future as well as discuss some of the present-day solutions.
- The Best and Worst Data Visualizations of 2018 - Feb 8, 2019.
We reflect on some of the best examples of Data Visualization throughout 2018, before focussing on some of the not-so-good and how these can be improved.
- Is Domain Knowledge a Hurdle to Start a Career in Data? - Feb 8, 2019.
How would I decide which domain to choose, while starting my career in data? Is it an obstacle?
- Neural Networks – an Intuition - Feb 7, 2019.
Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.
- Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry. - Feb 7, 2019.
The following is a method I developed, which is based on my personal experience managing a data-science-research team and was tested with multiple projects. In the next sections, I’ll review the different types of research from a time point-of-view, compare development and research workflow approaches and finally suggest my work methodology.
-
Top 10 Technology Trends of 2019 - Feb 7, 2019.
This article outlines 10 top trending technologies for 2019, a list which covers diverse topics such as security, IoT, reinforcement learning, energy sustainability, smart cities, and much more. - How I used NLP (Spacy) to screen Data Science Resumes - Feb 6, 2019.
A real life example of when using NLP can help filter down a list of candidates for a job opening, with full source code and methodology.
- The Essential Data Science Venn Diagram - Feb 4, 2019.
A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.
- Five Ways Your Safety Depends on Machine Learning - Feb 2, 2019.
Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.
- Data Scientists: Why are they so expensive to hire? - Feb 1, 2019.
We provide some reasoning behind the high cost factor of hiring a data scientist, including the increasing amount of data ready to be analyzed, the structural shortage of people with the appropriate skills, and more.
4 Reasons Why Your Machine Learning Code is Probably Bad
Learn How to Listen: One of the hardest parts of being a data scientist
Top 10 Technology Trends of 2019