Deep Learning

Deep Learning:



http://www.deeplearningbook.org/



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what are your diamonds

There was a story of two explorer's who went on a expedition to Africa. They saw little African kids playing marbles with large stone rocks and so the explorer's offered to trade those stones for some candy. The kids agreed and gave their stones to the explorer's. Amazingly, those stones were huge uncut diamonds! Unfortunately, those boys were unaware what they had and gave them away...

what are your diamonds...your gifts??? -Les Brown

Scripps Wired for Health study results show no clinical or economic benefit from digital health monitoring | MobiHealthNews

Scripps Wired for Health study results show no clinical or economic benefit from digital health monitoring | MobiHealthNews: " Better data visualizations and monitoring that is passive, rather than requiring manual logging action on the part of the patient, could go a long way."

The results are in for the Scripps Translational Science Institute’s Wired For Health study, and there’s no sugar-coating it: they’re disappointing for those working in digital health. The six-month randomized control trialfound no short-term benefit in health costs or outcomes for patients monitoring their health with connected devices. 

“It was a bit disappointing, but remember, this was the first multisensor trial that’s ever been reported, so in that respect it was a pioneering effort,” study author and STSI Director Dr. Eric Topol told MobiHealthNews. “And you know, it was very difficult because we had these three different sensors, glucose, blood pressure, and heart rhythm, and a lot of patients had all three problems or two of them, and had to have a dashboard created. There are a lot of logistical challenges there.”

Scripps recruited 160 patients with either hypertension, diabetes, or arrhythmia and randomized them into an intervention group and a control group. Members of the intervention group were issued an iPhone 4 and a connected device: for those with hypertension, a Withings blood pressure monitor, a Sanofi IBGStar blood glucose meter for those with diabetes, or an AliveCor ECG for those with arrhythmia. The study took place in 2013 and 2014.

In order to make data from their connected devices available to their physicians and their family members, intervention group participants also had access to Qualcomm Life’s HealthyCircles care coordination and management platform.

The study looked at claims data, to see whether one group cost more than the other, as well as outcomes data like blood pressure and HbA1C. They found no difference between the two groups. The only meaningful difference the study found was related to health self-management. Patients who monitored their health were less likely to attribute health outcomes to chance than those who didn’t monitor their health. Topol said several different surveys showed the monitoring patients had a better sense of wellbeing and felt more control over their health.

“We learned a lot, but the fact that it wasn’t a positive trial, with respect to reducing economic burden is probably not so surprising because we only followed the people for six months, and many would project that you need much more follow-up and a much larger sample size to be able to show the burden,” Topol said. “The good part is we didn’t add any burden economically. A lot of people thought, if people have access to their data they’re going to end up tapping more into medical resources. Well, we certainly didn’t see that. So that was encouraging, but obviously we would have liked to reduce the need for emergency rooms and office visits and hospitalizations. That could still be out there, but this is just the beginning of studying that question.”

There are a few possible explanations for the failure of digital health devices to impact outcomes or costs in this trial. One is that the trial only tested for short-term outcomes and healthcare costs after six months. It’s possible that this technology can have an effect over the longer term. Another is that this study more or less tested the effect of monitoring itself, although there were nurses and coaches made available to participants.

Topol also pointed out all the ways the technology has advanced since this study. Better data visualizations and monitoring that is passive, rather than requiring manual logging action on the part of the patient, could go a long way.

“I think the key here is that it’s the first foray into this multi-sensor smartphone world and we need to do a lot more randomized trials that are bigger and better. Hopefully I and many others will do this in the future,” he said.

Tags: 

Scripps, Scripps Translational Science Institute, STSI, Wired for Health, Sanofi iBGStar, AliveCor ECG, Withings blood pressure, Qualcomm Life Healthy Circles

Women in Tech: Gayle Laakmann McDowell excels beyond the stereotypes | ZDNet

http://www.zdnet.com/article/women-in-tech-gayle-laakmann-mcdowell-excels-beyond-the-stereotypes/

Gayle Laakmann McDowell is the founder and CEO of CareerCup.com, which offers technical interview preparation for software engineers, and is the author of “The Google Resume” and "Cracking the Coding Interview." She has previously worked as software engineer at Microsoft, Google, and Apple, holds a bachelor's and master's in Computer Science, and an MBA from the Wharton School.

Summarize your experience and what you do now. Please give a brief summary of your current role.

I've worked as a software engineer at Google, Microsoft, and Apple. I eventually got bored of working for large companies, and decided to try something more entrepreneurial. I joined a small VC-funded start-up as the VP of Engineering, and then got an MBA from the Wharton School. I also launched my own business, CareerCup.com, which helps people prepare for interviews at a tech companies. As Founder / CEO, my responsibilities range from coding the website / forum, to business development, to marketing and advertising.

Do you think that being a statistical minority in the Tech world has given you that extra push that you needed to become a top performer in your field?

Being a minority has neither encouraged me nor discouraged me. A long time ago, someone told me something that's stuck with me ever since: "Everyone will be judged for something." And it's so true. Women might be assumed to be less technical, but technical men are often assumed to have poor communication skills. Obviously, some people face more "-isms" than others, but everyone will need to push past some stereotyping in order to achieve what they want.

How did you choose the technical field from all other possibilities that were presented to you?

I had always been good at math and science, so something more quantitative was a natural fit for me. Software development in particular stood out because it allowed me to create rather than just analyze and understand what was already there. Programming to me was like a grown-up version of legos - except that you could do much cooler stuff.

Do you think that the tech field provides the opportunity for you to think more creatively or to innovate more freely than other fields?

Absolutely! People in the tech field are in an amazing place right now. Technology is rapidly evolving, and there are no signs of it slowing down soon. And, additionally, technology is the backbone of so much of the world right now and is making an enormous impact on it. It's incredible to be part of this. It opens up for so much innovation and creativity because, frankly, no one knows what the "right" answers are. Everyone has the chance to be exceptional.

If you were asked to mentor a young woman interested in a tech career, how would advise her?

She's made a fantastic decision in considering technology. It's an amazing time to be in this field and, frankly, the world needs more technologists (and especially more female techies).

She should be aware of two things though.

First, she should know that she's going to face some stereotyping - that's just how it is - but there are good things about being a woman in technology as well. There are a lot of people who will go out of their way to help women, which can open doors for you. Additionally, since there aren't many women, people are more likely to remember you. You don't blend in, and that can make it much easier to build a strong network.

Second, she should know that the best way to get a great education is to blend formal learning (courses, etc) with independent learning (projects, etc). Classes will give you the foundation of knowledge; without them, you may not know what you don't know. But learning on your own through projects will reinforce your skills and will show you how things happen in the world. Doing just one type of learning will limit you; do both.

Outside of technical skills, what other skills are important to be successful in technology?

I am a big believer in having strong communication skills - written and verbal - in addition to technical skills. Communication skills will set you apart from the pack.

Learn how to write well. This does not mean that you need to be able to beautiful, eloquent prose; in fact, that is often a hindrance. Writing well just means being able to communicate your point clearly and concisely.

Verbal communication skills, and interpersonal skills in general, will help you build a strong network. It will also enable you to effectively advocate on behalf of yourself, your team, or your company.

You've mentioned the value of a network several times. Why do you feel this is so important, and what advice would you give for building a good network?

Contrary to popular belief, most people do not get jobs through their friends. They get jobs through their friends of friends. Of course, these friends of friends are only reachable if you have a good network to start with.

A good network is filled with people who (1) are willing to help you and (2) are able to help you.

The first part of this, having people who are willing to help you, means that you need to be willing to help others too. People are likely to return the favor.

The second part of this, having people who are able to help you, means that you should be open to everyone you meet - even if you don't see immediately how they might be helpful. A diverse network is a powerful one.

So get out there, and start networking. Focus on helping the people you meet and you'll build your way to a very powerful network.

====

Gayle Laakmann McDowell's interviewing expertise comes from vast experience on both sides of the desk. She has completed Software Engineering interviews with - and received offers from - Microsoft, Google, Amazon, Apple, IBM, Goldman Sachs, Capital IQ, and a number of other firms.

Of these top companies, she has worked for Microsoft, Apple and Google, where she gained deep insight into each company's hiring practices. 

Most recently, Gayle spent three years at Google as a Software Engineer and was one of the company's lead interviewers. She interviewed over 120 candidates in the U.S. and abroad, and led much of the recruiting for her alma mater, the University of Pennsylvania.

Additionally, she served on Google's Hiring Committee, where she reviewed each candidate's feedback and made hire / no-hire decisions.

She assessed over 700 candidates in that role, and evaluated hundreds more resumes.

In 2005, Gayle founded CareerCup.com to bring her wealth of experience to candidates around the world. Launched first as a free forum for interview questions, CareerCup now offers a book, a video and mock interviews.

Gayle holds a bachelor's and master's degree in Computer Science from the University of Pennsylvania and an MBA from The Wharton School.

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AGI-Adjusted Gross Income Calculator

AGI-Adjusted Gross Income Calculator:



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Acoustic scene classification - DCASE2016

Acoustic scene classification - DCASE2016:



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Intel Core i7-6950X Broadwell-E Launches in 2nd Quarter 2016

Intel Core i7-6950X Broadwell-E Launches in 2nd Quarter 2016:



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Voice Isolation: When Noise Reduction Meets Deep Learning | EE Times

http://www.eetimes.com/author.asp?section_id=36&doc_id=1328589



Voice Isolation: When Noise Reduction Meets Deep Learning | EE Times: "Voice Isolation: When Noise Reduction Meets Deep Learning"

A new approach to old problems -- with the help of deep neural networks -- may make background noise a thing of the past.

Sometimes it's easy to forget that a smartphone is also a telephone. With all the fantastic functions and features, somehow people have grown accustomed to the occasional dropped syllable and garbled sounds that make us repeat ourselves time and again.A recent article in Scientific American suggests that the fault is with the service providers. It's true that bandwidth is definitely a factor, but even when there is a relatively good connection, throw in a noisy environment like a coffee shop or morning traffic and communication starts to break down. A new approach to old problems -- with the help of deep neural networks -- may make background noise a thing of the past.

The voice band -- good enough?
Since the invention of the telegraph almost two centuries ago, there has been nearly exponential improvement in bandwidth, mobility, speed, and reliability. That said, a key aspect of voice telecommunications has lagged behind: the quality and intelligibility of transmitted voice. Very early on, the standard for human voice transmission was set as the "voice band" located between 300 Hz and 3.3 kHz (to put this in perspective, the natural frequency span of human voice during speech ranges from about 50 Hz to nearly 10 kHz).

(Source: Unsplash.com)

Apparently, this was satisfactory for landline usage in quiet settings, and generations of phone users came to expect poor call quality. When these standards were carried over for cellphone audio quality, and with the added woes of spotty network coverage and connection dropouts, cellphone users' expectations for call quality fell even lower.

Extending the frequency (for better or for worse)
Now that there are about about as many cellphone subscriptions as there are people on earth, one would think that there really shouldn't be any more technological excuses for poor voice quality. New standards branded as HD Voice and VoLTE promise the eventual extension of voice transmission frequency range up to 7 kHz. An IEEE Spectrum article from September 2014 gave an instructive, in-depth analysis of the causes of lousy voice quality, and placed hope in the deployment of these new technologies. Their implementation requires new hardware and new networks, which will be overcome in time, but broadening the voice band does nothing to solve the other major challenge preventing great sounding calls -- in fact, HD Voice and its relatives may actually make the problem worse!

Noise and the boundless crusade for its cancellation
Nearly half of all phone users today employ their mobile phones as their primary voice connection (a number sure to grow). Mobile phones, by design, are used in many different environments: in planes, trains, and automobiles; at sporting events, offices, factories, and shopping centers; on playgrounds and (yeah, thatguy) in public restrooms.

Just think of the noises you might encounter walking through an urban downtown, near a construction site, or in an airport lounge. While the narrow range of the current voice band standard impairs the quality of the voice that is transmitted, it also automatically filters out any noise that may be present in higher frequency bands. By doubling the frequency span of the voice band, HD Voice and relatives increase the environmental noise power that is transmitted and, ironically, can make voice quality and intelligibility worse in everyday use cases.

(Source: Unsplash.com)

The noise challenges facing cellphone users are a far cry from the relatively stable noise environments that exist around landlines and, by-and-large, noise reduction technologies have not caught up. From commonly-used phase cancellation, to techniques using multiple microphones or statistical properties and mathematical assumptions about environmental noise, each attempt to isolate and cancel out noise has its deficiencies. Either some of the noise gets through or the voice suffers from audio artifacts.

Voice isolation instead of noise cancellation
The engineering team at Cypher took a different tack when developing its noise reduction technology. Instead of formulating the problem as one of capturing a signal and then eliminating the noise, they considered its mathematical dual: how to characterize and isolate the speech components of a noisy signal. Rather than trying to defend against all of the possible noise types -- an impossible task -- Cypher concentrates on elucidating and extracting common elements of human speech.

At the core of this approach is a sophisticated deep learning methodology that identifies mathematical descriptors, which can be used in training neural networks for audio pattern recognition. The deep learning stage takes place offline using a large database of human speech. The goal of the learning is to identify and separate human speech from any environmental noise.

(Source: CEVA)

The result is a deep neural network that can identify in real-time precisely when and where in an audio signal the human voice is present. Despite its broad and robust pattern recognition capabilities, this deep neural network is fast enough and compact enough to run in software on the CEVA-TeakLite-4 DSP. The neural network also guides other algorithmic components of Cypher's patented technology as they isolate the person speaking from all other sources of noise -- even other nearby human speakers. Once the desired voice has been extracted, post-processing modules enhance the voice signal and remove artifacts created in the background noise elimination process. The final output has a balanced, full sound as close to the original speaker as possible. To experience the clarity, visit Cypher's website for demonstrations of this cutting edge technology in a variety of different environments.

Eran Belaish serves as CEVA's Marketing Manager of Audio and Voice Product Line, overseeing Audio and Voice processing, Android interfaces, wearable devices, and Wireless Audio. Prior to this position, Eran served as CEVA's Senior Compiler Group Leader responsible for managing all compiler-related research and development, and before that he held several engineering and management positions at CEVA since 2003. Eran holds a B.Sc. in Electrical Engineering and Computer Science from Tel-Aviv University.

Dr. Erik Sherwood serves as Cypher's Chief Scientist. Erik is an applied mathematician with expertise in dynamical systems, computational neuroscience, scientific computing, algorithm design, statistics, and machine learning. Prior to joining Cypher, he worked in academia and held teaching, research, and faculty positions in the mathematics departments of Cornell University, Boston University, and, most recently, the University of Utah. Erik studied at Princeton, the University of Bremen (Germany), Cambridge University, and Cornell University. He earned an AB with honors in mathematics and certificates in applied mathematics, computer science, and German from Princeton University, and MS and PhD degrees in applied mathematics from Cornell University.

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Machine learning / data science 面经以及一些总结

关键字: data science,data scientist,machine learning,面经

发信站: BBS 未名空间站 (Fri Jan  8 11:52:21 2016, 美东)
Source: http://www.mitbbs.com/article_t/JobHunting/33120253.html




本着国人互助以及传递正能量的真理，发一下我个人找工作过程中整理的machine 
learning相关面经以及一些心得总结。楼主的背景是fresh CS PhD in computer 
vision and machine learning, 非牛校。

已经有前辈总结过很多machine learning的面试题(传送门： http://www.mitbbs.com/article/JobHunting/32808273_0.html)，此帖是对其的补充，有一小部分是重复的。面经分两大块：machine learning questions 和 coding questions.

Machine learning related questions:
-  Discuss how to predict the price of a hotel given data from previous 
years
-  SVM formulation
-  Logistic regression
-  Regularization
-  Cost function of neural network
-  What is the difference between a generative and discriminative algorithm
-  Relationship between kernel trick and dimension augmentation
-  What is PCA projection and why it can be solved by SVD  
-  Bag of Words (BoW) feature
-  Nonlinear dimension reduction (Isomap, LLE)
-  Supervised methods for dimension reduction
-  What is naive Bayes
-  Stochastic gradient / gradient descent
-  How to predict the age of a person given everyone’s phone call history
-  Variance and Bias (a very popular question, watch Andrew’s class)
-  Practices: When to collect more data / use more features / etc. (watch 
Andrew’s class)
-  How to extract features of shoes
-  During linear regression, when using each attribute (dimension) 
independently to predict the target value, you get a positive weight for 
each attribute. However, when you combine all attributes to predict, you get
some large negative weights, why? How to solve it?
-  Cross Validation
-  Reservoir sampling
-  Explain the difference among decision tree, bagging and random forest
-  What is collaborative filtering 
-  How to compute the average of a data stream (very easy, different from 
moving average)
-  Given a coin, how to pick 1 person from 3 persons with equal probability.


Coding related questions:
-  Leetcode: Number of Islands
-  Given the start time and end time of each meeting, compute the smallest 
number of rooms to host these meetings. In other words, try to stuff as many
meetings in the same room as possible
-  Given an array of integers, compute the first two maximum products(乘积) 
of any 3 elements (O(nlogn))
-  LeetCode: Reverse words in a sentence (follow up: do it in-place) 
-  LeetCode: Word Pattern
-  Evaluate a formula represented as a string, e.g., “3 + (2 * (4 - 1) )”
-  Flip a binary tree
-  What is the underlying data structure for JAVA hashmap? Answer: BST, so 
that the keys are sorted.
-  Find the lowest common parent in a binary tree
-  Given a huge file, each line of which is a person’s name. Sort the names
using a single computer with small memory but large disk space
-  Design a data structure to quickly compute the row sum and column sum of 
a sparse matrix  
-  Design a wrapper class for a pointer to make sure this pointer will 
always be deleted even if an exception occurs in the middle
-  My Google onsite questions: http://www.mitbbs.com/article_t/JobHunting/33106617.html

面试的一点点心得：
最重要的一点，我觉得是心态。当你找了几个月还没有offer，并且看到别人一直在版
上报offer的时候，肯定很焦虑甚至绝望。我自己也是，那些报offer的帖子，对我来说
都是负能量，绝对不去点开看。这时候，告诉自己四个字：继续坚持。我相信机会总会
眷顾那些努力坚持的人，付出总有回报。
machine learning的职位还是很多的，数学好的国人们优势明显，大可一试, 看到一些
帖子说这些职位主要招PhD，这个结论可能有一定正确性。但是凭借我所遇到的大部分
面试题来看，个人认为MS或者PhD都可以。MS的话最好有一些学校里做project的经验。
仔细学习Andrew Ng在Coursera上的 machine learning课，里面涵盖很多面试中的概念
和题目。虽然讲得比较浅显，但对面试帮助很大。可以把video的速度调成1.5倍，节省
时间。
如果对一些概念或算法不清楚或者想加深理解，找其他的各种课件和视频学习，例如
coursera，wiki，牛校的machine learning课件。
找工作之前做好对自己的定位。要弄清楚自己想做什么，擅长做什么，如何让自己有竞
争力，然后取长补短（而不是扬长避短）。
感觉data scientist对coding的要求没有software engineer那么变态。不过即便如此
，对coding的复习也不应该松懈。


我个人觉得面试machine learning相关职位前需要熟悉的四大块：
Classification:
Logistic regression
Neural Net (classification/regression)
SVM
Decision tree
Random forest
Bayesian network
Nearest neighbor classification

Regression:
Neural Net regression
Linear regression
Ridge regression (add a regularizer)
Lasso regression
Support Vector Regression
Random forest regression
Partial Least Squares

Clustering:
K-means
EM
Mean-shift
Spectral clustering
Hierarchical clustering

Dimension Reduction:
PCA
ICA
CCA
LDA
Isomap
LLE
Neural Network hidden layer

最后祝各位好运。那些还在继续找工作的亲们，坚持住，加油！

Apple Buys AI Startup That Reads Emotions in Faces | WIRED

Apple Buys AI Startup That Reads Emotions in Faces | WIRED:

In December, when WIRED spoke to Andrew Moore, the dean of computer science at Carnegie Mellon, he said that 2016 would be the year that machines learn to grasp human emotions. Now, right on cue, Apple has acquired Emotient, a startup that uses artificial intelligence to analyze your facial expressions and read your emotions.

First reported by The Wall Street Journal, the deal is notable because, well, it’s Apple, the world’s most valuable company and one of the most powerful tech giants. It’s unclear how Apple intends to use the company, but as Moore indicates, the tech built by Emotient is part of much larger trend across the industry. Using what are called deep neural networks—vast networks of hardware and software that approximate the web of neurons in the human brain—companies like Google and Facebook are working on similar face recognition technology and have already rolled it into their online services.

'There are huge implications in terms of making dialogue with computers much more meaningful.'

“We have very real data points showing computers doing a better job than humans in accessing emotional states,” Moore said in December. “There are huge implications in terms of making dialogue with computers much more meaningful.”

Moore points our that such technology can be used for everything from security to accessing mental heath. As the Journal explains, Emotient sold its tech to advertisers, letting them analyze how consumers responded to their ads. According to the startup, doctors have also used the technology to determine patient pain, and retailers have used it to track how shoppers react to products in stores.

With deep neural nets, machines can learn to do certain tasks by analyzing large amounts of data. If you feed enough photos of someone smiling into a neural net, for instance, it can learn to understand when someone is happy. And these techniques can be applied to more than just images. They have also proved successful with speech recognition and, to a certain extent, natural language understanding.

Google and Facebook and Microsoft are at the forefront of this deep learning movement. But Apple been pushing in the same direction. In the fall, Apple acquired a startup called VocalIQ, which uses deep neural nets for speech recognition. You might not be able to hide your true feelings from Siri for much longer.

Reference：

http://www.bloomberg.com/news/articles/2016-01-07/apple-buys-startup-that-sees-what-s-behind-your-smile


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读Ph.D.,做科研工作的优点 -- 绿卡数据分析 - 未名空间(mitbbs.com)

读Ph.D.,做科研工作的优点 -- 绿卡数据分析 - 未名空间(mitbbs.com): "，来到美国以后取得绿卡的情况是怎么样的呢：按照美国国务院公布的数字，取得
绿卡的人里面，有两万六千左右是在广州办理的亲属移民签证（这里包括美国公民收养
的中国小孩，每年两千多一点）"



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8 Signs You Are Smarter Than Average

8 Signs You Are Smarter Than Average:



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