BigDataFr recommends: Applications of Deep Learning […] This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of […]
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[arXiv] BigDataFr recommends: Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis
BigDataFr recommends: Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications Subjects: Computers and Society (cs.CY); Learning (cs.LG); Social and Information Networks (cs.SI) […] In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big […]
[Datasciencecentral – Top] BigDataFr recommends: 40 Techniques Used by Data Scientists
BigDataFr recommends: 40 Techniques Used by Data Scientists […] These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection […]
[arXiv] BigDataFr recommends: Limited Random Walk Algorithm for Big Graph Data Clustering
BigDataFr recommends: Limited Random Walk Algorithm for Big Graph Data Clustering Subjects: Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph) […]Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach […]
[Datasciencecentral] BigDataFr recommends: The challenges of word embeddings #deeplearning
BigDataFr recommends: The challenges of word embeddings […] In recent times deep learning techniques have become more and more prevalent in NLP tasks; just take a look at the list of accepted papers at this year’s NAACL conference, and you can’t miss it. We’ve now completely moved away from traditional NLP approaches to focus on […]
[Datasciencecentral] BigDataFr recommends: Hitchhiker’s Guide to Data Science, Machine Learning, R, Python
BigDataFr recommends: Hitchhiker’s Guide to Data Science, Machine Learning, R, Python […] Thousands of articles and tutorials have been written about data science and machine learning. Hundreds of books, courses and conferences are available. You could spend months just figuring out what to do to get started, even to understand what data science is about. […]
[arXiv – MSc Thesis] BigDataFr recommends: LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios
BigDataFr recommends: LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios Subjects: Machine Learning (stat.ML); Information Retrieval (cs.IR); Social and Information Networks (cs.SI) […]The purpose if this master’s thesis is to study and develop a new algorithmic framework for Collaborative Filtering to produce recommendations in the top-N recommendation problem. Thus, we propose Lanczos Latent […]
[Datasciencecentral] BigDataFr recommends: Mary Meeker, Analytics, and the Future of the Internet
BigDataFr recommends: Mary Meeker, Analytics, and the Future of the Internet […] It’s that time of year again when Mary Meeker, the great seer of the internet once again releases her annual Internet Trends 2016 report. If by chance you don’t know who Ms. Meeker is she is a partner in the VC firm Kliener […]
[arXiv – Tip] BigDataFr recommends: D-SPACE4Cloud: A Design Tool for Big Data Applications
BigDataFr recommends: D-SPACE4Cloud: A Design Tool for Big Data Applications Subjects: Distributed, Parallel, and Cluster Computing […]The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their […]
[Datasciencecentral] BigDataFr recommends: Stream Processing and Streaming Analytics – How It Works
BigDataFr recommends: Stream Processing and Streaming Analytics – How It Works […] Recently we started exploring the basics of Event Stream Processing (ESP) in our article Stream Processing – What Is It and Who Needs It. There we explained ESP capabilities, technologies, platforms, and business cases. There’s one more piece of information that you need […]