Editor9;s Note

While we are celebrating our fascinating community growth every month, we are also so happy to announce ML Weekly team is growing with strong .
Join me and welcome Christopher Dossman, ML researcher that joined us from this issue.

Alireza Samar

Editor9;s Pick

Apple-Picking Robot Prepares to Compete for Farm Jobs

Orchard owners say they need automation because seasonal farm labor is getting harder to come by.


News & Articles

AI Everywhere

Nvidia CEO Jensen Huang talks artificial intelligence, neural interfaces and holodecks.


TensorFlow Released a Set of Benchmarks


Compressing and Regularizing Deep Neural Networks

“deep compression significantly reduces the computation and storage required by neural networks. For example, for a convolutional neural network with fully connected layers, such as Alexnet and VGGnet, it can reduce the model size by 35x-49x. Even for fully convolutional neural networks such as GoogleNet and SqueezeNet, deep compression can still reduce the model size by 10x. Both scenarios results in no loss of prediction accuracy.”


Call for Papers

Machine Learning for Music Discovery

International Conference on Learning (ICML)
Sydney, Australia – 10 or 11 August 2017
Abstracts deadline: June 9, 2017


Papers & Tutorials

Deep Learning Papers by task

Cut through the noise with this list of the state of the art in ML algorithms broken down by use case.


Visual Attribute Transfer through Deep Image Analogy

Are you ready for anime styled episodes of Seinfeld? New art style algorithm based on VGG19 blows competition out of water taking the titles of State of the Art in style transfer.
Check out the applications here.


Exploring the Structure of High-Dimensional Data with HyperTools in Kaggle Kernels

“how can we harness the incredible pattern-recognition superpowers of our brains to visualize complex and high-dimensional datasets?”


A Semismooth Newton Method for Fast, Generic Convex Programming

Code for this paper.


for Beginners

A Beginner's Guide to Neural Networks in Python and SciKit Learn

Very well explained tutorial on NN with Python and SciKit Learn for those who kicking off in this field.



BioMedical Data Scientist @ Medal

Boston Office, Medal HQ (San Francisco)


Fullstack (Python) Engineer @ Medal

Boston Office, Medal HQ (San Francisco)


Senior DevOps/Systems Engineer @ Medal

Boston, Medal HQ (San Francisco)


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