Archive for September, 2016

22
Sep
SYNERGIC PARTNERS TO BE CITY PARTNER AT THE 5TH EDITION OF BIG DATA WEEK IN SPAIN
  • Synergic Partners
  • 1736 Views
  • 0 Comment
  • Barcelona . Big Data . Big Data Consulting . Big Data Week . Business . Carme Artigas . Columbia University . Company . Data . Data Analytics . Data Science . Data visualization . digital transformation . Events . Madrid . Meet Up . Rene Baston . Roger Magoulas . Sharon Sputz . Spain . Sponsor . Synergic Partners . Talent .

Synergic Partners will hold the 5th edition of Big Data Week in Spain, specifically as a City Partner in both Madrid and Barcelona. The event will take place the week of October 24-30, simultaneously with other cities including London, Chicago, Jakarta, Ho Chi Minh, etc. Big Data Week is a global platform for the most unique and important events for interconnected communities whose objective is to reveal and promote Big Data and new mass data analysis techniques as well as the study of its impact on social, political, commercial and technological levels. The initiative was started in 2011 by Stewart Townsend and, ever since then, for one week experts from various fields come together and cover the main sectors of…

17
Sep
Warnings about normalizing data
  • Santiago Morante
  • 4052 Views
  • 13 Comments
  • Algorithms . Algoritmos . Analítica Avanzada . Big Data . Blog Synergic . Data . Data Normalization . Data Science . datos . Machine Learning . Normalización de Datos .

For many machine learning algorithms, normalizing the data of analysis is a must. A supervised example would be neural networks. It is known that normalizing the input data to the networks improve the results. If you don't believe me it's ok (no offense taken), but you may prefer to believe Yann Le Cunn (Director of AI Research in Facebook and founding father of convolutional networks) by checking section 4.3 of this paper. You can catch up the idea with the first sentence of the section: Convergence [of backprop] is usually faster if the average of each input variable over the training set is close to zero. Among other things, one reason is that when the neural network tries to correct…

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