Feb
2018 Mar Opinions, Interviews
All (107) | Courses, Education (7) | Meetings (19) | News, Features (11) | Opinions, Interviews (25) | Top Stories, Tweets (9) | Tutorials, Overviews (32) | Webcasts & Webinars (4)
- Semantic Segmentation Models for Autonomous Vehicles
- Mar 29, 2018.
State-of-the-art Semantic Segmentation models need to be tuned for efficient memory consumption and fps output to be used in time-sensitive domains like autonomous vehicles.
- Principles of Guided Analytics
- Mar 27, 2018.
KNIME outline their guided analytics system and explain how this can assist data scientists to predict future outcomes.
- Comparing Deep Learning Frameworks: A Rosetta Stone Approach
- Mar 26, 2018.
A Rosetta Stone of deep-learning frameworks has been created to allow data-scientists to easily leverage their expertise from one framework to another.
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5 Things You Need to Know about Sentiment Analysis and Classification
- Mar 23, 2018.
We take a look at the important things you need to know about sentiment analysis, including social media, classification, evaluation metrics and how to visualise the results. - Data Skills: They’re Not Just for Data Scientists
- Mar 22, 2018.
The continued growth of big data, both in terms of quality and accessibility, is disrupting a wide range of roles. The skills needed to analyse this data need to be learned by everyone - not just data scientists.
- 8 Common Pitfalls That Can Ruin Your Prediction
- Mar 21, 2018.
A good prediction can help your work and make it easier. But how can you be sure that your prediction is good? Here are some common pitfalls that you should avoid.
- What Machine Learning Isn’t
- Mar 20, 2018.
There are limits to what the state-of-the-art is capable of, which doesn’t mean that there aren’t tons of perfect use cases for machine learning, but does mean that you have to go into the process with your eyes open.
- 5 Things You Need to Know about Big Data
- Mar 16, 2018.
We take a look at five things you need to know about Big Data.
- Creating a simple text classifier using Google CoLaboratory
- Mar 15, 2018.
Google CoLaboratory is Google’s latest contribution to AI, wherein users can code in Python using a Chrome browser in a Jupyter-like environment. In this article I have shared a method, and code, to create a simple binary text classifier using Scikit Learn within Google CoLaboratory environment.
- So, How Many Machine Learning Models You Have NOT Built?
- Mar 14, 2018.
Investigating how data scientists approach machine learning and applying this to the 'ship repair man' analogy.
- Will GDPR Make Machine Learning Illegal?
- Mar 14, 2018.
Does GDPR require Machine Learning algorithms to explain their output? Probably not, but experts disagree and there is enough ambiguity to keep lawyers busy.
- KDnuggets – Favorite Data Science / Machine Learning Blog
- Mar 12, 2018.
In a recent Kaggle Machine Learning and Data Science Survey, KDnuggets was no. 1 among favorite Data Science Blogs, Podcasts, or Newsletters.
- Hierarchical Classification – a useful approach for predicting thousands of possible categories
- Mar 12, 2018.
A detailed look at the flat and hierarchical classification approach to dealing with multi-class classification problems.
- How StockTwits Applies Social and Sentiment Data Science
- Mar 9, 2018.
StockTwits is a social network for investors and traders, giving them a platform to share assertions and perceptions, analyses and predictions.
- 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning
- Mar 8, 2018.
For the 2018 international women's day, we profile 18 inspiring women who lead the field in AI, Analytics, Big Data , Data science, and Machine Learning areas.
- Get ready for smart apps
- Mar 8, 2018.
Mobile platforms are set to benefit from Deep Learning this year, with significant improvements in privacy, offline functionality and much more. But which Android phone should you purchase to maximise these benefits?
- Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box
- Mar 8, 2018.
The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.
- Is an AI /machine-driven world better than a human driven world?
- Mar 7, 2018.
On the positive side of AI we have a prospect of self-driving cars, and other benefits, and thru education humans can evolve and improve. The risks include loss of jobs, growing inequality, dealing with superintelligence.
- The Two Sides of Getting a Job as a Data Scientist
- Mar 7, 2018.
Are you a Data Scientist looking for a Job? Are you a Recruiter looking for a Data Scientist? If you answered yes or NO to this questions you need to read this.
- Blockchains and APIs
- Mar 6, 2018.
Major technological advances are providing opportunities for new business models, based on blockchain, which will see an increase in the number of connected devices in our day-to-day lives.
- Should You Ever Volunteer Your Data Skills for Free?
- Mar 6, 2018.
The question has probably come up of whether it’s ever okay to offer your data-related knowledge to people or organizations for free. Does taking that approach ever benefit you?
- A Few Statistics Tips for Marketers
- Mar 6, 2018.
Statistics can help good marketers become better marketers. Here are a few things they should know about stats.
- Deep Misconceptions About Deep Learning
- Mar 5, 2018.
I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others.
- How to Survive Your Data Science Interview
- Mar 1, 2018.
There are many wonderful things about data science. It’s extreme breadth is not one of them. The title of data scientist means something different at every company
- Four Broken Systems & Four Tech Trends for 2018
- Mar 1, 2018.
We may be well into 2018, but here are a set of tech trends for looking forward, along with a set of 4 systems that manifested how inappropriate, inaccurate or outright broken they are in 2017.
Bayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning
A Summary of DeepMind’s Protein Folding Upset at CASP13