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 […]
Documentation
[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 […]
[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: 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 […]
[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 […]
[arXiv] BigDataFr recommends: Sparse p-Adic Data Coding for Computationally Efficient and Effective Big Data Analytics
BigDataFr recommends: Sparse p-Adic Data Coding for Computationally Efficient and Effective Big Data Analytics […]We develop the theory and practical implementation of p-adic sparse coding of data. Rather than the standard, sparsifying criterion that uses the L0 pseudo-norm, we use the p-adic norm. We require that the hierarchy or tree be node-ranked, as is standard […]
[Datasciencecentral] BigDataFr recommends: Deep Learning: Definition, Resources, Comparison with Machine Learning
BigDataFr recommends: Deep Learning: Definition, Resources, Comparison with Machine Learning […] Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision […]
[Techcrunch] BigDataFr recommends: Strengthening authentication through big data
BigDataFr recommends: Strengthening authentication through big data […]The fact that plain passwords are no longer safe to protect our digital identities is no secret. For years, the use of two-factor authentication (2FA) and multi-factor authentication (MFA) as a means to ensure online account security and prevent fraud has been a hot topic of discussion. Technological […]
[Data Science Central] BigDataFr recommends: 5 Obstacles Companies Face When Adopting a New Analytics Strategy
BigDataFr recommends: 5 Obstacles Companies Face When Adopting a New Analytics Strategy […] Analytics is a general term for a number of different processes, all of which are geared toward better understanding your demographics (or industry in general), and making improvements to your business’s infrastructure to account for those new insights. Unfortunately, whenever a company […]