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Everything a Data Scientist Should Know About Data Management

For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.

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#Data

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149 views19:13

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The Measure of #Intelligence, (Chollet 2019)
(François Chollet is the creator of #Keras)

Link to the paper

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154 views19:50

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Jeff Dean discusses the future of machine learning at TF World ‘19

#MachineLearning

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YouTube

Jeff Dean discusses the future of machine learning at TF World ‘19 (TensorFlow Meets)

AI Advocate Laurence Moroney sits down with Google Senior Fellow, Jeff Dean following his keynote presentation at TensorFlow World. They discuss how advances...

146 views19:53

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#Python developers survey 2019

Hey #Pythonista,

This is the third iteration of the official Python Developers Survey. With this survey, we aim to identify how the Python development world looks today and how it compares to last year. In 2018 we received 20,000 responses from Python developers, who shared their experience to help us map out an accurate landscape of the Python community.

The results of this survey serve as a major source of knowledge about the current state of the Python community, so we encourage you to participate and take this 10-minute survey and make an invaluable contribution to the community.

After the survey is over, we will publish the aggregated results and randomly choose 100 winners (from those who complete the survey in its entirety), who will each receive an amazing Python Surprise Gift Pack.

Thank you for contributing to this community effort!

Let’s get started with the survey!

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150 views20:06

Disney's Magic Highway - 1958

See how Walt #Disney in 1958 magically predicted future highways!

Link to the video in YouTube

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#Magic!
#SelfDrivingCars

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154 viewsedited 00:23

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A new very cool paper by Google:

Self-training with Noisy Student improves ImageNet classification

Abstract

We present a simple self-training method that achieves
87.4% top-1 accuracy on ImageNet, which is 1.0% better
than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves
ImageNet-A top-1 accuracy from 16.6% to 74.2%, reduces
ImageNet-C mean corruption error from 45.7 to 31.2, and
reduces ImageNet-P mean flip rate from 27.8 to 16.1.
To achieve this result, we first train an EfficientNet model
on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then
train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate
this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not
noised so that the pseudo labels are as good as possible.
But during the learning of the student, we inject noise such
as data augmentation, dropout, stochastic depth to the student so that the noised student is forced to learn harder from
the pseudo labels.

Link to the paper

#ComputerVision
#Google

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arXiv.org

Self-training with Noisy Student improves ImageNet classification

We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which...

170 views00:26

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