[unable to retrieve full-text content]The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.
Original Post: How to Organize Data Labeling for Machine Learning: Approaches and Tools
[unable to retrieve full-text content]Recently, PSL Research University launched a one-week course combining theoretical lectures and practical sessions. 115 students from various backgrounds and skill levels were enrolled; something quite spectacular happened during the week: Students have achieved an astounding level of score improvement – in just three afternoons.
Original Post: Open Innovation and Crowdsourcing in Machine Learning – Getting premium value out of data
[unable to retrieve full-text content]Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.
Original Post: 7 Ways to Get High-Quality Labeled Training Data at Low Cost
[unable to retrieve full-text content]Big data craze inspires firms to save every possible bit of data, with the misconception that the more data you have, the better. Firms must keep data (for compliance purposes) or often aren’t sure what information they need to keep. This post looks at alternative data sources.
Original Post: Data Hoarding and Alternative Data In Finance – How to Overcome the Challenges
Topic modelling is an important statistical modelling technique to discover abstract topics in collection of documents. This article talks about a new measure for assessing the semantic properties of statistical topics and how to use it. By Fred Morstatter and Huan Liu. Machine learning algorithms can help produce models that are capable of revealing summaries of the dataset. Topic modelling…
Original Post: Measuring Topic Interpretability with Crowdsourcing
Previous post Next post Tweet Tags: Acquisitions, Crowdsourcing, Datasets, startups An interesting discussion of the myriad methods in which startups may choose to acquire data, often the most overlooked and important aspect of a startup’s success (or failure). By Moritz Mueller-Freitag, Eleven Strategy. The “unreasonable effectiveness” of data for machine-learning applications has been widely debated over the…
Original Post: 10 Data Acquisition Strategies for Startups
Previous post Next post Tweet Tags: Big Data, Crowdsourcing, Datasets, IoT, Mobile, Sensors CrowdSignals.io a crowdfunding campaign to generate the largest mobile and sensor dataset available to the Data Science community for use in research and product development. By Evan Welbourne, CrowdSignals.io Seattle Start-Up AlgoSnap launches CrowdSignals.io, a crowdfunding campaign to generate the largest mobile and sensor dataset available…
Original Post: CrowdSignals.io, Building Big Mobile Social Sensor dataset
Tweet Previous post Next post Tags: Alexy Khrabrov, Crowdsourcing, Google, Knowledge Graph Currently, only global corporations like Google or Facebook can maintain a vast knowledge graph about the world. Little companies which rely on knowing world context need to unite to create a Public Knowledge Graph, or they will fall further behind the big guys. comments By…
Original Post: Public Knowledge Graph – small guys unite