Discovering Information with a Little Help from Tweet Assistant

If you noticed a spike in my Twitter posts today, there is a reason.  I have been searching #machinelearning using Tweet Assistant (more on Twitter Assistant in a future post)

These tweets, hashtags, mentions and popular posts provide a gold mine of information.  However, there is more to analyzing tweets than getting news and opinions. Within minutes, I was following some cool people and started discovering awesome tweets and visualizations.

Machine Learning – Top 10 Mentions

 

Machine Learning – Top 5 Hashtags

I started retweeting some of the posts I found but felt that there are far too many posts and a discussion of these may fill a couple of blog posts. So that is what I am going to do.

 

 

Technology in Farming – Robots, Mixed Reality, Machine Learning

Is manual farming sustainable as the need for agricultural products grow in demand? Can technology help? How does it impact lives of farmers? Is it the right thing to do? Like any other applications of technology, there are more questions than answers. The following links are just a set of leading indicators of trends.

Agricultural vehicles known as “cucumber flyers” enable as many as 50 seasonal workers to harvest crops.
Experts from Fraunhofer IPK in Berlin, along with other German and Spanish researchers, are studying the potential for automating cucumber harvests in the scope of the EU project CATCH, which stands for “Cucumber Gathering – Green Field Experiments.” Project partners are the Leibniz Institute for Agricultural Engineering and Bioeconomy in Germany and the CSIC-UPM Centre for Automation and Robotics (CAR) in Spain.
During the Hands Free Hectare project, no human set foot on the field between planting and harvest—everything was done by robots. This includes:
  • Drilling channels in the dirt for barley seeds to be planted at specific depths and intervals with an autonomous tractor;
  • Spraying a series of fungicides, herbicides, and fertilizers when and where necessary;
  • Harvesting the barley with an autonomous combine.

How mixed reality and machine learning are driving innovation in farming

The Economist, in its Q2 Technology Quarterly issue, proclaims agriculture will soon need to become more manufacturing-like in order to feed the world’s growing population. Scientific American reports crops will soon need to become more drought resistant in order to effectively grow in uncertain climates. Farms, The New York Times writes, will soon need to learn how to harvest more with less water.

A Resource for Machine Learning

Metacademy is a community-driven, open-source platform for experts to collaboratively construct a web of knowledge. Right now, Metacademy focuses on machine learning and probabilistic AI, because that’s what the current contributors are experts in. But eventually, Metacademy will cover a much wider breadth of knowledge, e.g. mathematics, engineering, music, medicine, computer science…

Current focus is on machine learning and probabilistic AI.

Metacademy could not exist without a large amount of high quality learning resources, so the true credit goes to these experts. Nevertheless, Roger Grosse and Colorado Reed built this site and organized roughly 350 machine learning and probabilistic AI concepts that comprise the intellectual seed that will allow Metacademy to flourish into a globally accessible and understandable learning experience.

You can find more about the concepts related to the topic of Machines Learning in this Concept List

Data Science – A Few Tweets and Links

What is Data Science?

What is Data Science from Wikipedia Talks a bit of the history as well.

What is data science? – O’Reilly Radar

Data Science Courses and Recipes

Coursera Introduction to Data Science Course

RT @radar: Want to be a data wrangler? School of Data offers free online data science  courses

Applications, Tools

If you are wondering about the applications of Data Science, please watch the first couple of videos from this course

RT @StartupYou: DIY Data Science – when will this happen and think of how big it will be

Data Science Tools: Tools slowly democratize many data science tasks

“Deep Learning – The Biggest Data Science Breakthrough of the Decade” – Free webcast from O’Reilly

Tim O’Reilly – “Data science is transformative. The first wave was marketing analytics, before that financial arbitrage.”

Mapping Twitter’s Python and Data Science Communities

Data science and the analytic lifecycle  by @bigdata #strataconf

Other Resources

A bitty bundle of data science blogs Collected by @hmason. via @mikeloukides Call for more http://t.co/2iFABfIl2q (look at the comments in the blog for more resources links)

What’s A ‘Data Scientist’ Anyway? Real-Time With m6d’s Claudia Perlich”

Machine Learning – A Few Links and Tweets

On Machine Learning from A free book on ML – A First Encounter of Machine Learning by Max Welling

The first reason for the recent successes of machine learning and the growth of the field as a whole is rooted in its multidisciplinary character. Machine learning emerged from AI but quickly incorporated ideas from fields as diverse as statistics, probability, computer science, information theory, convex optimization, control theory, cognitive science, theoretical neuroscience, physics and more.
The second, perhaps more important reason for the growth of m
achine learning is the exponential growth of both available data and computer power. While the field is build on theory and tools developed statistics machine learning recognizes that the most exiting progress can be made to leverage the enormous flood of data that is generated each year by satellites, sky observatories, particle accelerators, the human genome project, banks, the stock market, the army, seismic measurements, the internet, video, scanned text and so on.

On why this book was written

Much of machine learning is built upon concepts from mathematics such as partial derivatives, eigenvalue decompositions, multivariate probability densities and so on. I quickly found that these concepts could not be taken for granted at an undergraduate level.

Machine learning will be one of the most important tech trends over the next three to five years for innovation” http://t.co/kBFPHlANHa

Startups making machine learning an elementary affair http://t.co/FkF7TSy45R

Use Cases Machine Learning on Big Data for Predictive Analytics http://t.co/1AvQHXkgr4 #ml usecases

A startup journey, the improvement in Python’s data science capabilities and hosted machine learning http://t.co/Vx4g7lIM1X #techtrends

RT @woycheck: Zico Kolter wants to use machine learning to analyze electrical current behavior and provide details about your power bill (@…

Microsoft Research Machine Learning Summit: April 22-24, 2013 http://t.co/x9YxylgMeX

RT @siah: A free ebook by Max Welling “A First Encounter with Machine Learning” http://t.co/5KjCCylL3Y

Google Hires Brains that Helped Supercharge Machine Learning | Wired Enterprise | http://t.co/cVgZpNri4c http://t.co/2mJ7ggZE2n

RT @siah: PyMADlib: A Python wrapper for MADlib – an open source library for scalable in-database machine learning algorithms http://t.c

Peekaboo: Machine Learning Cheat Sheet (for scikit-learn) http://t.co/6UyYWO74

Panels and Discussions

This is a panel from Churchill Club featuring
Peter Norvig, Director of Research, Google ,Gurjeet Singh, Co-founder & CEO, Ayasdi, Jeremy Howard, President and Chief Scientist, Kaggle

Meta

Once in a while, I go and gather my recent tweets and create a Tweet Cloud (a project developed by a student). I find some interesting topics, save the tweets and start a blog. I have written about this Linked Tweet Cloud a couple of times.

tweets_on_machine_learning

LinkLog: Deep Learning and the Google Brain

From The Man Behind the Google Brain

With Deep Learning, Ng says, you just give the system a lot of data “so it can discover by itself what some of the concepts in the world are.” Last year, one of his algorithms taught itself torecognize cats after scanning millions of images on the internet. The algorithm didn’t know the word “cat” — Ng had to supply that — but over time, it learned to identify the furry creatures we know as cats, all on its own.

This approach is inspired by how scientists believe that humans learn. As babies, we watch our environments and start to understand the structure of objects we encounter, but until a parent tells us what it is, we can’t put a name to it.

LinkLog: Google Prediction API

The Prediction API enables access to Google’s machine learning algorithms to analyze your historic data and predict likely future outcomes. Upload your data to Google Storage for Developers, then use the Prediction API to make real-time decisions in your applications. The Prediction API implements supervised learning algorithms as a RESTful web service to let you leverage patterns in your data, providing more relevant information to your users. Run your predictions on Google’s infrastructure and scale effortlessly as your data grows in size and complexity.

You can read more from the Google Labs Prediction API page

One of the cool things about this API is that it can be accessed from several apps:

Accessible from many platforms: Google App Engine, Apps Script (Google Spreadsheets), web & desktop apps, and command line

Plan to check it out and see how it compares with Open Calais from Reuters. Google is making it more and more compelling to use their APIs and App Engine.