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BigData News Sunday, March 4 Data science, Deep data science, New business opportunities & more…

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BigData News TLDR / Table of Contents

  • 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 vend…
    • data science, deep data science, data science equivalent, data science techniques, closely related fields
  • How 5G Creates New Business Opportunities for Operators and ICT Players
    • Learn how operators can take advantage of the business opportunities and revenue streams created by 5G. I’ll use Ericsson’s report “The Industry Impact of 5G” to explain.
    • new business opportunities, operators, , ,
  • 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
  • How To Build a Machine Learning Classifier in Python with Scikit-learn | DigitalOcean
    • Machine learning is a research field in computer science, artificial intelligence, and statistics. The focus of machine learning is to train algorithms to learn patterns and make predictions from data. Machine learning is especially valuable because i
    • machine, data, machine learning, breast cancer, following results
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    • resources allocation, contact support, website, ,

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40 Techniques Used by Data Scientists

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  • Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science.
  • Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics.
  • Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus also belong to deep data science.
  • However, these techniques are not starred here, as the standard versions of these techniques are more well known (and unfortunately more used) than the deep data science equivalent.
  • Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation.

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]40 Techniques Used by Data Scientists – Data Science Central[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”How-5G-Creates-New-Business-Opportunities-for-Operators-and-ICT-Players”][vc_column width=”1/2″][vc_separator][vc_column_text]

How 5G Creates New Business Opportunities for Operators and ICT Players

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  • In this post, Ill focus on how operators (and other ICT players) can take advantage of the new business opportunities and revenue streams created by 5G.
  • In order to understand how 5G creates new business opportunities for operators, you have to first flip your thinking upsidedown.Ive read several blog posts that talk about the new business opportunities for operators that come with 5G, but what makes 5G different?
  • Ill be referring to it often for the duration of thispost.For this report, Ericsson asked 900 decision makers in large companies across 10 key industries about: – The role they expect 5G to play within their sectorThe problems they expect 5G to solveThe extent to which theyd be willing to…
  • This report provides useful insight for any operator looking to capitalize on the new business opportunities and revenue streams that come with 5G.
  • Increase their understanding of how 5G user needs evolveIncrease the scope of network services offered beyond raw broadband connectivitySplit the market into micro-segments to discover latent user and industry needsIncrease the number of use cases addressed with a customized value propositionIncrease differentiation in network service pricing to meet each microsegments…

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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=”How-To-Build-a-Machine-Learning-Classifier-in-Python-with-Scikit-learn-DigitalOcean”][vc_column width=”1/2″][vc_separator][vc_column_text]

How To Build a Machine Learning Classifier in Python with Scikit-learn | DigitalOcean

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  • To get a better understanding of our dataset, let’s take a look at our data by printing our class labels, the first data instance’s label, our feature names, and the feature values for the first data instance: – – ML Tutorial – … – – # Look at our see…
  • Import the function and then use it to split the data: – – ML Tutorial – … – – from sklearn.model_selection import train_test_split – – # Split our data – train, test, train_labels, test_labels = train_test_split(features, – labels, – test_size=0.33, – random_state=42) – – The function randomly splits the data…
  • Then initialize the model with the GaussianNB() function, then train the model by fitting it to the data using gnb.fit(): – – ML Tutorial – … – – from sklearn.naive_bayes import GaussianNB – – # Initialize our classifier – gnb = GaussianNB() – – # Train our classifier – model…
  • Use the predict() function with the test set and print the results: – – ML Tutorial – … – – # Make predictions – preds = the code and you’ll see the following results: – – – – – – As you see in the Jupyter Notebook output, the predict()…
  • The final version of the code should look like this: – – ML Tutorial – – from sklearn.datasets import load_breast_cancer – from sklearn.model_selection import train_test_split – from sklearn.naive_bayes import GaussianNB – from sklearn.metrics import accuracy_score – – # Load dataset – data = load_breast_cancer() – – # Organize our data…

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]How To Build a Machine Learning Classifier in Python with Scikit-learn | DigitalOcean[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Account-Suspended”][vc_column width=”1/2″][vc_separator][vc_column_text]

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