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BigData News Wednesday, March 14 Machine learning, Dimensional data sets, Network & more…

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BigData News TLDR / Table of Contents

  • Machine Learning Vs. Statistics
    • This article was written by Aatash Shah.Many people have this doubt, what’s the difference between statistics and machine learning? Is there something like…
    • machine learning, dimensional data sets, statistics, data analytics standpoint, Statistical Learning Theory
  • How I implemented iPhone X’s FaceID using Deep Learning in Python.
    • One of the most discussed features of the new iPhone X is the new unlocking method, the successor of TouchID: FaceID. Having created a bezel-less phone, Apple had to develop a new method to unlock…
    • network, siamese neural network, deep learning, user, neural networks
  • Selected Recent Articles from Top DSC Contributors – Part 7
    • This is a new series, featuring great content from our top contributors. Some of these articles are rather technical in nature, but many are business-oriented…
    • data science, his/her profile picture, exploratory data analysis, Data Science Central, Naive Bayes Classification

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Machine Learning Vs. Statistics

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  • Many peoplehave this doubt, whats the difference between statistics and machine learning?
  • Both machine learning and statistics have the same objective: – – They are both concerned with the same question: how do we learn from data?
  • In his blog, he states how the same concepts have different names in the two fields, – – Nowadays, both machine learning and statistics techniques are used in pattern recognition, knowledge discovery and data mining.
  • Source:SAS Institute;A Venndiagram that shows how machine learning and statistics are related – – Both machine learning and statistics share the same goal: learning from data.
  • For more machine learning statistics related articles on DSC clickhere.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: machine learning, dimensional data sets, statistics, data analytics standpoint, Statistical Learning Theory[/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”]Machine Learning Vs. Statistics – Data Science Central[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”How-I-implemented-iPhone-X-s-FaceID-using-Deep-Learning-in-Python-“][vc_column width=”1/2”][vc_separator][vc_column_text]

How I implemented iPhone X’s FaceID using Deep Learning in Python.

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  • I was very intrigued by the techniques used by Apple to realize FaceID, especially by the fact that this all runs on-device, with a little initial training on the users face, and then runs smoothly every time the phone is picked up.
  • Apple Keynote unveiling iPhone X andFaceID.Performing classification, for a neural network, means learning to predict if the face it has seen its the userss one or not.
  • This would require a lot of time, energy consumption, and impractical availability of training data of different faces to have negative examples (little would change in case of transfer learning and fine tuning of an already trained network).
  • Instead, I believe FaceID is powered by a siamese-like convolutional neural network that is trained offline by Apple to map faces into a low-dimensional latent space shaped to maximize distances between faces of different people, using a contrastive loss.
  • Contrastive loss.After some training, the network is able to map faces into 128-dimensional arrays, such that pictures of the same person are grouped together, while being far from pictures of other persons.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: network, siamese neural network, deep learning, user, neural networks[/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 I implemented iPhone X’s FaceID using Deep Learning in Python.[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Selected-Recent-Articles-from-Top-DSC-Contributors—Part-7″][vc_column width=”1/2″][vc_separator][vc_column_text]

Selected Recent Articles from Top DSC Contributors – Part 7

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  • Some of these articles are rather technical in nature, but many are business-oriented and written in simple English.
  • The entire series consists of about 120 articles.
  • We intend to publish a new set every two weeks or so.
  • To read more articles from a same author, read one of his/her articles and click on his/her profile picture to access the full list.
  • Some of these articles are curated or posted as guest blogs.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: data science, his/her profile picture, exploratory data analysis, Data Science Central, Naive Bayes Classification[/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”]Selected Recent Articles from Top DSC Contributors – Part 7 – Data Science Central[/vc_column_text][/vc_column][/vc_row]

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