Git has rapidly become the standard in distributed source control. Find out how to use it, and while we’re at at, what’s this GitHub stuff all about?

Jason Axelson (http://hicapacity.org) will introduce you to the Git version control system. He’ll also cover GitHub, and how you can begin using it today.

If there’s time after the talk, we can take questions and perhaps share some l337 hax0r Git tips among the crowd. Also, consider this a good segue to our GitHub speaker in September.

RSVP: http://www.meetup.com/dynamic/events/72498612/

Tuesday, July 17, 2012 @ 7:00 PM

The Box Jelly - 307c Kamani Street (map)

Hey makers,

Things are moving forward at HI Capacity, I am proud to announce that we will be getting a dedicated space within a month! We are aiming to be fully moved in by early July. We will be moving in to the current Box Jelly space and they will be expanding to take over the whole side store that is parallel to Fishcake. Since we are a member-funded organization that means that our monthly fees will increase by $15/month to handle the increased rent and utilities. So the standard price is changing from $35 -> $50 and the premium “help us grow” membership is changing from $50 -> $75. But if you are signed up by the end of June you will be able to keep the current prices for 6 months so sign up now!

With the dedicated space we will have expanded hours and finally be able to get some more serious equipment. We do not have any specific equipment purchases planned yet but we’re hoping to get a smaller cnc mill or lathe. Additionally a few members have equipment that they were willing to donate.

As a reminder here are some the things we have right now:

• Community! Meet cool people and those who pass through!
• Space for doing projects
• High-speed wireless internet
• HD Projector (for giving talks)
• Soldering tools (soldering irons, helping hands, solder)
• 3D Printers (MakerBot)
• iPhone Developers License

You can view a full list of membership perks on the site: http://hicapacity.org/perks/

We will be taking over a majority of the current Box Jelly space and will be able to utilize it 24/7. However, we will still be able to use the rest of the Box Jelly after hours (good for the programmers).

As part of the move, there will be a wall built between our area and the fishcake. HI Capacity will have to pay for the build out of the wall along with an AC unit (since we can no longer share the Fishcake’s unit). This build-out will cost a significant amount of money so we are planning a fundraiser to help pay for it and some equipment for the space.

This is an exciting time, let us know if you have any questions or concerns!

Jason

Kevin McCarthy will present a gentle introduction to Machine Learning.

Note day and location change, just for this meeting.

Tuesday, 6/26, 7pm - Interisland Terminal R/D

691 Auahi St., Honolulu, HI 96813 - http://goo.gl/maps/aTvk

RSVP Here: http://www.meetup.com/dynamic/events/69285152/

Have you ever wished your computer could do more than what you tell it to do explicitly? Maybe you want to write a recommendation engine like the one Amazon and Netflix use to recommend similar products, or maybe you just want to build Skynet. The goal of this talk is to give a broad but shallow overview of machine learning techniques and applications. Topics covered will (probably) include:

• What is machine learning?
• Supervised vs unsupervised machine learning
• Linear Regression
• Partitioning your data into training, test, and cross-validation sets
• Bias/variance tradeoff
• Regularization
• Logistic Regression
• Clustering
• Brief overview of more advanced algorithms such as neural networks and support vector machines
• Advanced applications such as digit recognition and collaborative filtering

Should be fun!

More on Machine Learning:

Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases.

From Wikipedia