Deep Embedding Kernel

--Abstract
In this paper, we propose a novel supervised learning method that is calledDeep Embedding Kernel (DEK). DEK combines the advantages of deep learning andkernel methods in a unified framework. More specifically, DEK is a learnablekernel represented by a newly designed deep architecture. Compared withpre-defined kernels, this kernel can be explicitly trained to map data to anoptimized high-level feature space where data may have favorable featurestoward the application. Compared with typical deep learning using SoftMax orlogistic regression as the top layer, DEK is expected to be more generalizableto new data. Experimental results show that DEK has superior performance thantypical machine learning methods in identity detection, classification,regression, dimension reduction, and transfer learning.

2018-04-16T17:25:24Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1804.05806v1

Split learning for health: Distributed deep learning without sharing raw patient data

--Abstract
Can health entities collaboratively train deep learning models withoutsharing sensitive raw data? This paper proposes several configurations of adistributed deep learning method called SplitNN to facilitate suchcollaborations. SplitNN does not share raw data or model details withcollaborating institutions. The proposed configurations of splitNN cater topractical settings of i) entities holding different modalities of patient data,ii) centralized and local health entities collaborating on multiple tasks andiii) learning without sharing labels. We compare performance and resourceefficiency trade-offs of splitNN and other distributed deep learning methodslike federated learning, large batch synchronous stochastic gradient descentand show highly encouraging results for splitNN.

2018-12-03T05:43:20Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1812.00564v1

Deep-learning technique reveals “invisible” objects in the dark

--Abstract
Method could illuminate features of biological tissues in low-exposure images.

December 12, 2018

@machinelearning_tuts
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Link : http://news.mit.edu//2018/deep-learning-dark-objects-1212

Opportunities for materials innovation abound

--Abstract
Faculty researchers share insights into new capabilities at the annual Industrial Liaison Program Research and Development Conference.

December 14, 2018

@machinelearning_tuts
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Link : http://news.mit.edu//2018/mit-industrial-liaison-program-conference-1214

Recent Advances in Deep Learning: An Overview

--Abstract
Deep Learning is one of the newest trends in Machine Learning and ArtificialIntelligence research. It is also one of the most popular scientific researchtrends now-a-days. Deep learning methods have brought revolutionary advances incomputer vision and machine learning. Every now and then, new and new deeplearning techniques are being born, outperforming state-of-the-art machinelearning and even existing deep learning techniques. In recent years, the worldhas seen many major breakthroughs in this field. Since deep learning isevolving at a huge speed, its kind of hard to keep track of the regularadvances especially for new researchers. In this paper, we are going to brieflydiscuss about recent advances in Deep Learning for past few years.

2018-07-21T15:40:10Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1807.08169v1

Geometric Understanding of Deep Learning

--Abstract
Deep learning is the mainstream technique for many machine learning tasks,including image recognition, machine translation, speech recognition, and soon. It has outperformed conventional methods in various fields and achievedgreat successes. Unfortunately, the understanding on how it works remainsunclear. It has the central importance to lay down the theoretic foundation fordeep learning. In this work, we give a geometric view to understand deep learning: we showthat the fundamental principle attributing to the success is the manifoldstructure in data, namely natural high dimensional data concentrates close to alow-dimensional manifold, deep learning learns the manifold and the probabilitydistribution on it. We further introduce the concepts of rectified linear complexity for deepneural network measuring its learning capability, rectified linear complexityof an embedding manifold describing the difficulty to be learned. Then we showfor any deep neural network with fixed architecture, there exists a manifoldthat cannot be learned by the network. Finally, we propose to apply optimalmass transportation theory to control the probability distribution in thelatent space.

2018-05-26T09:15:53Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1805.10451v2

Machine Learning Refined — J. Watt, R. Borhani, A. K. Katsaggelos (en) 2016
#book #middle #theory

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@drivelesscar
@autonomousvehicle

Machine Learning Refined — J. Watt, R. Borhani, A. K. Katsaggelos (en) 2016
#book #middle #theory

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@machinelearning_tuts
@drivelesscar
@autonomousvehicle

What is it?

This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.My main goal was to find an approach to studying Machine Learning that is mainly hands-on and abstracts most of the Math for the beginner. This approach is unconventional because it’s the top-down and results-first approach designed for software engineers.

https://www.datasciencecentral.com/profiles/blogs/top-down-learning-path-machine-learning-for-software-engineers?fbclid=IwAR0rOV5VXrJOQTY3BDoNPYBNubgpeQleRQDcchmf-Hena7_WYRJSu5zVd_U

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@machinelearning_tuts

#آموزش
In this tutorial, you will learn how to perform regression using Keras and Deep Learning. You will learn how to train a Keras neural network for regression and continuous value prediction, specifically in the context of house price prediction.

https://www.pyimagesearch.com/2019/01/21/regression-with-keras/
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@machinelearning_tuts

Logistic Regression: A Concise Technical Overview

Link: https://www.kdnuggets.com/2019/01/logistic-regression-concise-technical-overview.html

Practical Machine Learning – Sunila Gollapudi (en)
#book #middle #theory

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@drivelesscar
@autonomousvehicle

Practical Machine Learning – Sunila Gollapudi (en)
#book #middle #theory

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@machinelearning_tuts
@drivelesscar
@autonomousvehicle

❇️ مجموعه 10 کورس رایگان در حوزه دیتاساینس و یادگیری ماشین

1️⃣ Machine Learning
(University of Washington)
2️⃣ Machine Learning
(University of Wisconsin-Madison)
3️⃣ Algorithms (in journalism)
(Columbia University )
4️⃣ Practical Deep Learning
(Yandex Data School)
5️⃣ Big Data in 30 Hours
(Krakow Technical University )
6️⃣ Deep Reinforcement Learning Bootcamp
(UC Berkeley(& others))
7️⃣ Introduction to Artificial intelligence
(University of Washington)
8️⃣ Brains, Minds and Machines Summer Course
(MIT)
9️⃣ Design and Analysis of Algorithms
(MIT)
🔟 Natural Language Processing
(University of Washington)
لینک:
https://goo.gl/Riybxs

#MachineLearning #DataScience #Course #DeepLearning #BigData #AI


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@drivelesscar
@autonomousvehicle

7 Ways Artificial Intelligence Can Be Used in An Educational Setting
January 21, 2019 https://www.re-work.co/blog/7-ways-ai-can-be-used-in-education

You're on a journey to learn Data Science, Randy Lao is here to help you along the way!
watch free courses, download free books and learn more about machine learning every day...

#ml
#course
#resource

@machinelearning_tuts

http://www.claoudml.co/

Nice article by Dat Tran about some mathematicians trying to make sense of neural networks. Some of the findings are quite obvious to machine learning practitioners/researchers like deeper network with many layers and fewer neurons aka ResNet are better than shallow networks with few layers but many neurons per layer. It's still interesting though to see that there's an effort in trying to build a "general theory" of neural networks which one usually obtains from experiences and a lot of trial and error. Maybe this will help in the future to do less trial and error.

Dat Tran (https://www.linkedin.com/in/dat-tran-a1602320/)

#deeplearning
#machinelearning
#ml
#article

@machinelearning_tuts

image

https://www.quantamagazine.org/foundations-built-for-a-general-theory-of-neural-networks-20190131/

Artificial Intelligence and Games by Georgios N. Yannakakis

https://www.8freebooks.net/download-artificial-intelligence-and-games-georgios-n-yannakakis-pdf/

Which Deep Learning Framework is Growing Fastest? Read a comparison between major Deep learning frameworks in terms of demand, usage, and popularity https://www.kdnuggets.com/2019/05/which-deep-learning-framework-growing-fastest.html

New AI Strategy Mimics How Brains Learn to Smell

Today’s artificial intelligence systems, including the artificial neural networks broadly inspired by the neurons and connections of the nervous system, perform wonderfully at tasks with known constraints. They also tend to require a lot of computational power and vast quantities of training data. That all serves to make them great at playing chess or Go, at detecting if there’s a car in an image, at differentiating between depictions of cats and dogs. “But they are rather pathetic at composing music or writing short stories,” said Konrad Kording, a computational neuroscientist at the University of Pennsylvania. “They have great trouble reasoning meaningfully in the world.”

#deeplearning
#machinelearning
#brainmimic
#smelling

@machinelearning_tuts

For more information:
https://www.quantamagazine.org/new-ai-strategy-mimics-how-brains-learn-to-smell-20180918/