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BigData News Friday, March 2 Machine learning, Deep learning, Deep learning & more…

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BigData News TLDR / Table of Contents

  • List of Free Must-Read Machine Learning Books
    • Machine learning is an application of artificial intelligence that gives a system an ability to automatically learn and improve from experiences without being…
    • machine learning, deep learning, best free machine, statistical learning methods, Deep Learning textbook
  • When Not to Use Deep Learning
    • Guest blog by Pablo Cordero. Pablo is currently a postdoc at UCSC’s systems biology group, doing applied machine learning research in the context of cell biolo…
    • deep learning, deep net, deep nets, deep learning models, deep learning excels
  • 10 Tools for Data Visualizing and Analysis for Business
    • Digging through messy data and doing numerous calculations just so you can submit a report or arrive at the result of your quarterly business development can…
    • data analysis, data analysis tools, business data analysis, data visualization, best data analysis
  • ELT vs. ETL: Defining the Difference – Talend
    • The difference between ETL and ELT lies in where data is transformed into business intelligence and how much data is retained in working data warehouses. Discover what those differences mean for business intelligence, which approach is best for your organization, and why the cloud is changing everything.
    • ELT, data, business intelligence, big data, raw data
  • Big data is not enough
    • Quote from Brad Efron on why “big data” is not just “big.”
    • data, big data, data sets, gigantic data sets, genetic data

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List of Free Must-Read Machine Learning Books

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  • In this article, we have listed some of the best free machine learning books that you should consider going through (no order in particular).
  • This book holds the prologue to statistical learning methods along with a number of R labs included.
  • For the mathematics- savvy people, this is one of the most recommended books for understanding the magic behind Machine Learning.
  • This book has a lot to offer to the Engineering and Computer Science students studying Machine Learning and Artificial Intelligence.
  • Comment below with your list of some awesome machine learning books.

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]List of Free Must-Read Machine Learning Books – Data Science Central[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”When-Not-to-Use-Deep-Learning”][vc_column width=”1/2″][vc_separator][vc_column_text]

When Not to Use Deep Learning

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  • In sum, he argues that when the sample size is small (which happens a lot in the bio domain), linear models with few parameters perform better than deep nets even with a modicum of layers and hidden units.
  • Deep learning can really work on small sample sizes: – – Deep learnings claim to fame was in a context with lots of data (remember that the first Google brain project was feeding lots of YouTube videos to a deep net), and ever since it has constantly been publicized as…
  • There is also an aspect of deep learning models that I see gets sort of lost in translation when coming from other fields of machine learning.
  • Most tutorials and introductory material to deep learning describe these models as composed by hierarchically-connected layers of nodes where the first layer is the input and the last layer is the output and that you can train them using some form of stochastic gradient descent.
  • The optimization methods themselves receive little additional attention, which is unfortunate since its likely that a big (if not the biggest) part of why deep learning works is because of those particular methods (check out, e.g.this post from Ferenc Huszrsand this paper taken from that post), and knowing how to…

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]When Not to Use Deep Learning – Data Science Central[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”10-Tools-for-Data-Visualizing-and-Analysis-for-Business”][vc_column width=”1/2″][vc_separator][vc_column_text]

10 Tools for Data Visualizing and Analysis for Business

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  • Stanford Universitys own data analysis tool is open to public use.
  • Just keep in mind that you can use this tool of simple calculations and not vast sprawling data analysis tasks.
  • You might have heard about Zoho, since its one of the most popular business data analysis tools on the web.
  • While some of the features might be too advanced for everyday data analysis, NodeXL is the perfect tool for more complex tasks.
  • Another Google tool on our list that provides visualization and analysis but doesnt focus on raw data.

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]10 Tools for Data Visualizing and Analysis for Business – Data Science Central[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”ELT-vs-ETL-Defining-the-Difference—Talend”][vc_column width=”1/2″][vc_separator][vc_column_text]

ELT vs. ETL: Defining the Difference – Talend

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  • The difference between ETL and ELT lies in where data is transformed and how much data is retained in working data (ETL) is an integration approach that pulls information from remote sources, transforms it into defined formats and styles, then loads it into databases, data sources, or data (ELT) similarly…
  • ELT asks less of remote sources, requiring only their raw and unprepared data.
  • Both ETL and ELT are time-honored methodologies for producing business intelligence from raw data.
  • But, as with almost all things technology, the cloud is changing how businesses tackle ELT challenges.
  • Get started with ELT or ETL with Talends tools for Big Data.

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Big data is not enough

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  • Given enough data, correct answers jump out at you, right?
  • In some ways I think that scientists have misled themselves into thinking that if you collect enormous amounts of data you are bound to get the right answer.
  • You are not bound to get the right answer unless you are enormously smart.
  • You can narrow down your questions; but enormous data sets often consist of enormous numbers of small sets of data, none of which by themselves are enough to solve the thing you are interested in, and they fit together in some complicated way.

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