Home » Posts tagged 'network'

Tag Archives: network

New Free Online Book by Rob Hyndman on Forecasting using #Rstats

From the creator of some of the most widely used packages for time series in the R programming language comes a brand new book, and its online!

This time the book is free, will be updated and 7 chapters are ready (to read!)

. If you do forecasting professionally, now is the time to suggest your own use cases to be featured as the book gets ready by end- 2012. The book is intended as a replace­ment for Makri­dakis, Wheel­wright and Hyn­d­man (Wiley 1998).

http://otexts.com/fpp/

The book is writ­ten for three audi­ences:

(1) people find­ing them­selves doing fore­cast­ing in busi­ness when they may not have had any for­mal train­ing in the area;

(2) undergraduate stu­dents study­ing busi­ness;

(3) MBA stu­dents doing a fore­cast­ing elec­tive.

The book is dif­fer­ent from other fore­cast­ing text­books in sev­eral ways.

  • It is free and online, mak­ing it acces­si­ble to a wide audience.
  • It is con­tin­u­ously updated. You don’t have to wait until the next edi­tion for errors to be removed or new meth­ods to be dis­cussed. We will update the book frequently.
  • There are dozens of real data exam­ples taken from our own con­sult­ing prac­tice. We have worked with hun­dreds of busi­nesses and orga­ni­za­tions help­ing them with fore­cast­ing issues, and this expe­ri­ence has con­tributed directly to many of the exam­ples given here, as well as guid­ing our gen­eral phi­los­o­phy of forecasting.
  • We empha­sise graph­i­cal meth­ods more than most fore­cast­ers. We use graphs to explore the data, analyse the valid­ity of the mod­els fit­ted and present the fore­cast­ing results.

A print ver­sion and a down­load­able e-version of the book will be avail­able to pur­chase on Ama­zon, but not until a few more chap­ters are written.

Contents

(Ajay-Support the open textbook movement!)

If you’ve found this book helpful, please consider helping to fund free, open and online textbooks. (Donations via PayPal.)

Look for yourself at http://otexts.com/fpp/

 

Interview John Myles White , Machine Learning for Hackers

Here is an interview with one of the younger researchers  and rock stars of the R Project, John Myles White,  co-author of Machine Learning for Hackers.

Ajay- What inspired you guys to write Machine Learning for Hackers. What has been the public response to the book. Are you planning to write a second edition or a next book?

John-We decided to write Machine Learning for Hackers because there were so many people interested in learning more about Machine Learning who found the standard textbooks a little difficult to understand, either because they lacked the mathematical background expected of readers or because it wasn’t clear how to translate the mathematical definitions in those books into usable programs. Most Machine Learning books are written for audiences who will not only be using Machine Learning techniques in their applied work, but also actively inventing new Machine Learning algorithms. The amount of information needed to do both can be daunting, because, as one friend pointed out, it’s similar to insisting that everyone learn how to build a compiler before they can start to program. For most people, it’s better to let them try out programming and get a taste for it before you teach them about the nuts and bolts of compiler design. If they like programming, they can delve into the details later.

We once said that Machine Learning for Hackers  is supposed to be a chemistry set for Machine Learning and I still think that’s the right description: it’s meant to get readers excited about Machine Learning and hopefully expose them to enough ideas and tools that they can start to explore on their own more effectively. It’s like a warmup for standard academic books like Bishop’s.
The public response to the book has been phenomenal. It’s been amazing to see how many people have bought the book and how many people have told us they found it helpful. Even friends with substantial expertise in statistics have said they’ve found a few nuggets of new information in the book, especially regarding text analysis and social network analysis — topics that Drew and I spend a lot of time thinking about, but are not thoroughly covered in standard statistics and Machine Learning  undergraduate curricula.
I hope we write a second edition. It was our first book and we learned a ton about how to write at length from the experience. I’m about to announce later this week that I’m writing a second book, which will be a very short eBook for O’Reilly. Stay tuned for details.

Ajay-  What are the key things that a potential reader can learn from this book?

John- We cover most of the nuts and bolts of introductory statistics in our book: summary statistics, regression and classification using linear and logistic regression, PCA and k-Nearest Neighbors. We also cover topics that are less well known, but are as important: density plots vs. histograms, regularization, cross-validation, MDS, social network analysis and SVM’s. I hope a reader walks away from the book having a feel for what different basic algorithms do and why they work for some problems and not others. I also hope we do just a little to shift a future generation of modeling culture towards regularization and cross-validation.

Ajay- Describe your journey as a science student up till your Phd. What are you current research interests and what initiatives have you done with them?

John-As an undergraduate I studied math and neuroscience. I then took some time off and came back to do a Ph.D. in psychology, focusing on mathematical modeling of both the brain and behavior. There’s a rich tradition of machine learning and statistics in psychology, so I got increasingly interested in ML methods during my years as a grad student. I’m about to finish my Ph.D. this year. My research interests all fall under one heading: decision theory. I want to understand both how people make decisions (which is what psychology teaches us) and how they should make decisions (which is what statistics and ML teach us). My thesis is focused on how people make decisions when there are both short-term and long-term consequences to be considered. For non-psychologists, the classic example is probably the explore-exploit dilemma. I’ve been working to import more of the main ideas from stats and ML into psychology for modeling how real people handle that trade-off. For psychologists, the classic example is the Marshmallow experiment. Most of my research work has focused on the latter: what makes us patient and how can we measure patience?

Ajay- How can academia and private sector solve the shortage of trained data scientists (assuming there is one)?

John- There’s definitely a shortage of trained data scientists: most companies are finding it difficult to hire someone with the real chops needed to do useful work with Big Data. The skill set required to be useful at a company like Facebook or Twitter is much more advanced than many people realize, so I think it will be some time until there are undergraduates coming out with the right stuff. But there’s huge demand, so I’m sure the market will clear sooner or later.

The changes that are required in academia to prepare students for this kind of work are pretty numerous, but the most obvious required change is that quantitative people need to be learning how to program properly, which is rare in academia, even in many CS departments. Writing one-off programs that no one will ever have to reuse and that only work on toy data sets doesn’t prepare you for working with huge amounts of messy data that exhibit shifting patterns. If you need to learn how to program seriously before you can do useful work, you’re not very valuable to companies who need employees that can hit the ground running. The companies that have done best in building up data teams, like LinkedIn, have learned to train people as they come in since the proper training isn’t typically available outside those companies.
Of course, on the flipside, the people who do know how to program well need to start learning more about theory and need to start to have a better grasp of basic mathematical models like linear and logistic regressions. Lots of CS students seem not to enjoy their theory classes, but theory really does prepare you for thinking about what you can learn from data. You may not use automata theory if you work at Foursquare, but you will need to be able to reason carefully and analytically. Doing math is just like lifting weights: if you’re not good at it right now, you just need to dig in and get yourself in shape.
About-
John Myles White is a Phd Student in  Ph.D. student in the Princeton Psychology Department, where he studies human decision-making both theoretically and experimentally. Along with the political scientist Drew Conway, he is  the author of a book published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. He is also working with Mark Hansenon a book for laypeople about exploratory data analysis.John is the lead maintainer for several R packages, including ProjectTemplate and log4r.

(TIL he has played in several rock bands!)

—–
You can read more in his own words at his blog at http://www.johnmyleswhite.com/about/
He can be contacted via social media at Google Plus at https://plus.google.com/109658960610931658914 or twitter at twitter.com/johnmyleswhite/

New Amazon Instance: High I/O for NoSQL

Latest from the Amazon Cloud-

hi1.4xlarge instances come with eight virtual cores that can deliver 35 EC2 Compute Units (ECUs) of CPU performance, 60.5 GiB of RAM, and 2 TiB of storage capacity across two SSD-based storage volumes. Customers using hi1.4xlarge instances for their applications can expect over 120,000 4 KB random write IOPS, and as many as 85,000 random write IOPS (depending on active LBA span). These instances are available on a 10 Gbps network, with the ability to launch instances into cluster placement groups for low-latency, full-bisection bandwidth networking.

High I/O instances are currently available in three Availability Zones in US East (N. Virginia) and two Availability Zones in EU West (Ireland) regions. Other regions will be supported in the coming months. You can launch hi1.4xlarge instances as On Demand instances starting at $3.10/hour, and purchase them as Reserved Instances

http://aws.amazon.com/ec2/instance-types/

High I/O Instances

Instances of this family provide very high instance storage I/O performance and are ideally suited for many high performance database workloads. Example applications include NoSQL databases like Cassandra and MongoDB. High I/O instances are backed by Solid State Drives (SSD), and also provide high levels of CPU, memory and network performance.

High I/O Quadruple Extra Large Instance

60.5 GB of memory
35 EC2 Compute Units (8 virtual cores with 4.4 EC2 Compute Units each)
2 SSD-based volumes each with 1024 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
Storage I/O Performance: Very High*
API name: hi1.4xlarge

*Using Linux paravirtual (PV) AMIs, High I/O Quadruple Extra Large instances can deliver more than 120,000 4 KB random read IOPS and between 10,000 and 85,000 4 KB random write IOPS (depending on active logical block addressing span) to applications. For hardware virtual machines (HVM) and Windows AMIs, performance is approximately 90,000 4 KB random read IOPS and between 9,000 and 75,000 4 KB random write IOPS. The maximum sequential throughput on all AMI types (Linux PV, Linux HVM, and Windows) per second is approximately 2 GB read and 1.1 GB write.

Who made Who in #Rstats

While Bob M, my old mentor and fellow TN man maintains the website http://r4stats.com/ how popular R is across various forums, I am interested in who within R community of 3 million (give or take a few) is contributing more. I am very sure by 2014, we can have a new fork of R called Hadley R, in which all packages would be made by Hadley Wickham and you wont need anything else.

But jokes apart, since I didnt have the time to

1) scrape CRAN for all package authors

2) scrape for lines of code across all packages

3) allocate lines of code (itself a dubious software productivity metric) to various authors of R packages-

OR

1) scraping the entire and 2011′s R help list

2) determine who is the most frequent r question and answer user (ala SAS-L’s annual MVP and rookie of the year awards)

I did the following to atleast who is talking about R across easily scrapable Q and A websites

Stack Overflow still rules over all.

http://stackoverflow.com/tags/r/topusers shows the statistics on who made whom in R on Stack Overflow

All in all, initial ardour seems to have slowed for #Rstats on Stack Overflow ? or is it just summer?

No the answer- credit to Rob J Hyndman is most(?) activity is shifting to Stats Exchange

http://stats.stackexchange.com/tags/r/topusers


You could also paste this in Notepad and some graphs on Average Score / Answer or even make a social network graph if you had the time.

Do NOT (Go/Bi) search for Stack Overflow API or web scraping stack overflow- it gives you all the answers on the website but 0 answers on how to scrape these websites.

I have added a new website called Meta Optimize to this list based on Tal G’s interview of Joseph Turian,  at http://www.r-statistics.com/2010/07/statistical-analysis-qa-website-did-stackoverflow-just-lose-it-to-metaoptimize-and-is-it-good-or-bad/

http://metaoptimize.com/qa/tags/r/?sort=hottest

There are only 17 questions tagged R but it seems a lot of views is being generated.

I also decided to add views from Quora since it is Q and A site (and one which I really like)

http://www.quora.com/R-software

Again very few questions but lot many followers

Hacker Alert- Darpa project 10$ K for summer

If you bleed red,white and blue and know some geo-spatial analysis ,social network analysis and some supervised and unsupervised learning (and unlearning)- here is a chance for you to put your skills for an awesome project

 

from wired-

http://www.wired.com/dangerroom/2012/07/hackathon-guinea-pig/

 

For this challenge, Darpa will lodge a selected six to eight teams at George Mason University and provide them with an initial $10,000 for equipment and access to unclassified data sets including “ground-level video of human activity in both urban and rural environments; high-resolution wide-area LiDAR of urban and mountainous terrain, wide-area airborne full motion video; and unstructured amateur photos and videos, such as would be taken from an adversary’s cell phone.” However, participants are encouraged to use any open sourced, legal data sets they want. (In the hackathon spirit, we would encourage the consumption of massive quantities of pizza and Red Bull, too.)

 

DARPA Innovation House Project

Home | Data Access | Awards | Team Composition | Logisitics | Deliverables | Proposals | Evaluation Criteria | FAQ

PROPOSAL SUBMISSION

Proposals must be one to three pages. Team resumes of any length must be attached and do not count against the page limit. Proposals must have 1-inch margins, use a font size of at least 11, and be delivered in Microsoft Word or Adobe PDF format.

Proposals must be emailed to InnovationHouse@c4i.gmu.edu by 4:00PM ET on Tuesday, July 31, 2012.

Proposals must have a Title and contain at least the following sections with the following contents.

  1. Team Members

Each team member must be listed with name, email and phone.
The Lead Developer should be indicated.
The statement “All team members are proposed as Key Personnel.” must be included.

  1. Capability Description

The description should clearly explain what capability the software is designed to provide the user, how it is proposed to work, and what data it will process.

In addition, a clear argument should be made as to why it is a novel approach that is not incremental to existing methods in the field.

  1. Proposed Phase 1 Demonstration

This section should clearly explain what will be demonstrated at the end of Session I. The description should be expressive, and as concrete as possible about the nature of the designs and software the team intends to produce in Session I.

  1. Proposed Phase 2 Demonstration

This section should clearly explain how the final software capability will be demonstrated as quantitatively as possible (for example, positing the amount of data that will be processed during the demonstration), how much time that will take, and the nature of the results the processing aims to achieve.

In addition, the following sections are optional.

  1. Technical Approach

The technical approach section amplifies the Capability Description, explaining proposed algorithms, coding practices, architectural designs and/or other technical details.

  1. Team Qualifications

Team qualifications should be included if the team?s experience base does not make it obvious that it has the potential to do this level of software development. In that case, this section should make a credible argument as to why the team should be considered to have a reasonable chance of completing its goals, especially under the tight timelines described.

Other sections may be included at the proposers? discretion, provided the proposal does not exceed three pages.

[Top]

 

http://www.darpa.mil/NewsEvents/Releases/2012/07/10.aspx

 

 

 

Possible Digital Disruptions by Cyber Actors in USA Electoral Cycle

Some possible electronic disruptions  that threaten to disrupt the electoral cycle in United States of America currently underway is-

1) Limited Denial of Service Attacks (like for 5-8 minutes) on fund raising websites, trying to fly under the radar of network administrators to deny the targeted  fundraising website for a small percentage of funds . Money remains critical to the world’s most expensive political market. Even a 5% dropdown in online fund-raising capacity can cripple a candidate.

2)  Limited Man of the Middle  Attacks on ground volunteers to disrupt ,intercept and manipulate communication flows. Basically cyber attacks at vulnerable ground volunteers in critical counties /battleground /swing states (like Florida)

3) Electro-Magnetic Disruptions of Electronic Voting Machines in critical counties /swing states (like Florida) to either disrupt, manipulate or create an impression that some manipulation has been done.

4) Use search engine flooding (for search engine de-optimization of rival candidates keywords), and social media flooding for disrupting the listening capabilities of sentiment analysis.

5) Selected leaks (including using digital means to create authetntic, fake or edited collateral) timed to embarrass rivals or influence voters , this can be geo-coded and mass deployed.

6) using Internet communications to selectively spam or influence independent or opinionated voters through emails, short messaging service , chat channels, social media.

7) Disrupt the Hillary for President 2016 campaign by Anonymous-Wikileak sympathetic hacktivists.

 

 

FaceBook IPO- Who hacked whom?

Some thoughts on the FB IPO-

1) Is Zuck reading emails on his honeymoon? Where is he?

2) In 3 days FB lost 34 billion USD in market valuation. Thats enough to buy AOL,Yahoo, LinkedIn and Twitter (combined)

3) People are now shorting FB based on 3-4 days of trading performance. Maybe they know more ARIMA !

4) Who made money on the over-pricing in terms on employees who sold on 1 st day, financial bankers who did the same?

5) Who lost money on the first three days due to Nasdaq’s problems?

6) What is the exact technical problem that Nasdaq had?

7) The much deplored FaceBook Price/Earnings ratio (99) is still comparable to AOL’s (85) and much less than LI (620!). see http://www.google.com/finance?cid=296878244325128

8) Maybe FB can stop copying Google’s ad model (which Google invented) and go back to the drawing table. Like a FB kind of Paypal

9) There are more experts on the blogosphere than experts in Wall Street.

10) No blogger is willing to admit that they erred in the optimism on the great white IPO hope.

I did. Mea culpa. I thought FB is a good stock. I would buy it still- but the rupee tanked by 10% since past 1 week against the dollar.

 

I am now waiting for Chinese social network market to open with IPO’s. Thats walled gardens within walled gardens of Jade and Bamboo.

Related- Art Work of Another 100 billion dollar company (2006)

Follow

Get every new post delivered to your Inbox.

Join 744 other followers