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BigData News Friday, March 23 Deep learning, Machine learning, Data science & more…

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BigData News TLDR / Table of Contents

  • How to build a deep learning model in 15 minutes –
    • Introducing Lore, a Python framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers.
    • deep learning, machine learning, deep learning architecture, deep learning model, Machine Learning Researchers
  • Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics
    • In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, de…
    • data science, machine learning, data scientist, ,
  • Predictive Analytics Path to Mainstream Adoption
    • Hold on to your hats data scientists, you’re in for another wild ride.   A few months ago, our beloved field of predictive analytics was taken down a peg by t…
    • predictive analytics, data, Exploratory Data Analysis, predictive analytics problem, predictive analytics projects

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How to build a deep learning model in 15 minutes –

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  • A common feeling in Machine Learning: – Uhhh, this single sheet of paper does not tell me how this is supposed towork…Common ProblemsPerformance bottlenecks are easy to hit when youre writing bespoke code at high levels like Python or SQL.Code Complexity grows because valuable models are the result of many…
  • At Instacart, three of our teams are using Lore for all new machine learning development, and we are currently running a dozen Lore models in production.
  • If you like to see feature specs before you alt-tab to your terminal and start writing code, heres a brief overview: – Models support hyper parameter search over estimators with a data pipeline.
  • 3) Generate ascaffoldEvery lore Model consists of a Pipeline to load and encode the data, and an Estimator that implements a particular machine learning algorithm.
  • Finally, our model specifies the high level properties of our deep learning architecture, by delegating them back to the estimator, and pulls its data from the pipeline we built.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: deep learning, machine learning, deep learning architecture, deep learning model, Machine Learning Researchers[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]How to build a deep learning model in 15 minutes – tech-at-instacart[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Difference-between-Machine-Learning-Data-Science-AI-Deep-Learning-and-Statistics”][vc_column width=”1/2″][vc_separator][vc_column_text]

Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics

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  • In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
  • Before digging deeper into the link between data science and machine learning, let’s briefly discuss machine learning and deep learning.
  • If the data collected comes from sensors and if it is transmitted via the Internet, then it is machine learning or data science or deep learning applied to IoT.
  • Machine learning and statistics are part of data science.
  • For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any a-priori knowledge or training set to help the classification algorithm.

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics – Data Science Central[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Predictive-Analytics-Path-to-Mainstream-Adoption”][vc_column width=”1/2″][vc_separator][vc_column_text]

Predictive Analytics Path to Mainstream Adoption

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  • However, I get nervous when folks jump on the trend and try to apply predictive analytics blindly as a way to automate the solution of any problem with data.
  • For example, a company may use last years customer data to build a model which will predict which customers have a high potential to leave.
  • Customer attribute data such as demographics, spend and engagement are analyzed using statistical techniques to create a predictive model.
  • After the predictive model is created, it is then capable of taking in the same type of customer information (demographics, spend and engagement)for a new data set and estimating for each customer in this new data set, their probability of leaving.
  • For example; if Average Monthly Spend is a customer attribute in your data set, you will want to explore the following angles: – – Transform- Before you create a model, you need to perform some minor tweaks on your data set to maximize model performance.

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Predictive Analytics Path to Mainstream Adoption – Data Science Central[/vc_column_text][/vc_column][/vc_row]

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