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Mono 3.0.4 is out! Features include Improved garbage collection, Async bug fix, and Xamarin studio support.

Mono 3.0.4 released Greetings to all of you open source patrons out there! I've just received news of the latest release of Mono (3.0.4). The new release includes several major improvements and bug fixes. In this article, I'd like to provide a brief overview highlighting the major changes in the latest release of Mono. So without further ado, here is a quick overview of what's offered in this version of the Mono project. Improved garbage collection The GC implementation has been given a makeover. These changes include: A new approach called "cementing" has been added to the SGen concurrent garbage collector. Mono allocates all new small objects in a defined memory space referred to as the nursery. When a collection occurs, the surviving objects become root objects and are copied to the major heap. Typically, few references that are allocated to the nursery survive to become roots, so the majority of the objects are instantly collected which leaves plenty of allocation space for new objects. These nursery collections minimize the work that must be done by the collector. One of the problems with the garbage collection in previous versions of mono involved instances in which objects are "pinned" in the nursery (due to managed/unmanaged references or other operations). Objects that are "pinned" cannot be moved to the major heap. Typically the collector must keep track of these "pinned" objects (and their relationships) and it rescans them on each collection attempt to try to see if they have been released and are able to be moved. This approach was an inefficient practice of the collector. This is where cementing comes in to play. Cementing is a process by which references in the nursery that are pinned are simply marked as root objects, but they remain in the nursery since they can't be moved to the heap. This dramatically reduces overhead related to pinned nursery objects and their relationships. There are also several bug fixes related to garbage collection including #9928 pointer free deadlock problem and bugs in mono_gc_weak_link_get Improved StreamReader/StreamWritter asynchronous operations The asynchronous operations have been rewritten to resolve bug #9761. Which caused the operations to fail on subsequent calls. OSX Homebrew installation conflict resolution Mono no longer installs a /usr/bin/pkg-config file on OSX, which resolves an issue that effected Homebrew installations. The installation only contains the new Gtk+ stack that allows the new Xamarin Studio to run on OSX with 3.0. This is exciting news! Conclusion (for now) Well that about wraps it up.  Oh, one more thing..   In case you haven't heard, Xamarin has released Xamarian 2.0 which includes iOS development from within Visual Studio, a brand new IDE called Xamarin studio that is geared toward developing mobile apps for Android, and iOS. The IDE runs on Windows, Linux and OSX! I would like to mention that I will be delivering a detailed product review on the new and exciting features of Xamarin 2.0. So check back for my review and thanks for reading! Buddy kick it on  

Machine Learning: 5 examples of what it is and why you should care

Machine learning examples to make you think Hello folks, and welcome to another awe inspiring article from .  This article is one that I'm very excited to present.  I'm sure you've guessed by now that the topic of this post is Machine learning.   If you don't know what machine learning is or don't care, I ask you to take a look at the fascinating examples that I've presented in this article.  You just may get inspired.   Background I'm a .NET developer and I have experience working in a myriad of different business domains.  My love affair with machine learning was brought about while I was working for an e-commerce website.  My boss had asked me to look into a new API that Google had released in beta at the time that would allow you to provide data about your customers and it would suggest products based on their shopping data.  That API is called Google prediction and you can read about the Google prediction API here.  It's been a couple of years since I was introduced to this technology and since that time I've contemplated how machine learning algorithms work and what possibilities they could unlock with the right amount of data and creativity. Since then I've done a lot of reading, and planning on ways that I can collect data to use in my journey to learn as much as I can about this new frontier that we as software developers are facing.  I was fortunate enough become the first accepted team member of the open source project called NND, or Neural Network Designer by Bragisoft.  Check back soon for an article dedicated to this wonderful open source project on machine learning and neural networks. Proceed with caution Let it be known, however, that machine learning algorithms are not for the faint of heart.  This is a very complex array of concepts and I don't plan to  try to explain them in this article.  What I will do is give a brief, simple introduction to a few of the prevalent topics that one would need to research in order to implement machine learning algorithms.  The main purpose of this article is to provide some wonderful youtube videos that provide insight into the possibilities of machine learning and it's practical applications.  Some of the videos just may blow your mind! So without further ado, let's bring forth the videos! A glimpse at the future to whet your appetite  This first video is full of commentary and stunning examples of robots that lack brains but are capable of learning by way of a design that mimics a central nervous system.  Although the video depicts the future as something to be worried about, it's still a great, non technical introduction to whet your appetite and make way for the other more specific videos that are focused on simulations and applications of machine learning. Watch a simulation of robots that learn to drive In this video, we see a software demonstration of animated robots that start with no knowledge of a driving course.  Watch what happens as the simulation is processed repeatedly, allowing the robots to learn the course with each generation.  Buckle up! Hey Darwin, what do you think of this? This next video is progressively more complex, however, the simulation clearly shows how a collection of bots act as they evolve.  The bots  can attack each other with the intent of killing and eating each other to sustain life.  They also reproduce to make baby bots.  Take a look at this awesome example of machine learning.. Virtual Darwinism take two This next video is another representation of virtual evolution by machine learning techniques.  Watch block like organisms learn to fight each other over a virtual cube that represents a block of food.   Machine learning for pattern matching and recognition This video shows an application that will render CAPTCHA verification methods obsolete.  I have a decent amount of experience working with OCR engines however, this is the most accurate recognition I've ever seen. Show and tell This concludes my article on machine learning examples.  I hope you enjoyed the article and that it has peaked your interest in machine learning.  Do you have a favorite video or application that demonstrates machine learning, neural networks, prediction algorithms, pattern matching or some other related technology?  If so, we'd love to hear about it so please leave a comment with a link. Thanks for reading, Buddy James kick it on

dot42 Android development with C#. All the best parts with less restrictions!

Check out dot42 and Xamarin 2.0 forr developing Android applications using the .NET framework.
Check out dot42 and Xamarin 2.0 forr developing Android applications using the .NET framework. [More]

Complete coverage of your source code with NDepend part 1

What is NDepend? This article is part one of a two part series about one of the most practical and dynamic tools in existence for .NET development.  I’m talking about NDepend  I was approached about writing a review for NDepend so I downloaded the application to give it a try.  As with all of my reviews, let it be known that if I think a product is mediocre, then that’s what I’m going to write.  All that to say that this is no exaggeration, I really feel this strongly about this tool.  I’m sure by the end of this article, I will have peeked your interest too.  If you are interested, please read on. NDepend pro product suite From, “NDepend is a Visual Studio tool to manage complex .NET code and achieve high Code Quality.”  This tool allows you to visualize your source code in many different ways in an effort to analyze the quality of your code and how to improve it.  The product comes complete with a Visual Studio add in, an independent GUI tool, and a set of power tools that are console based which makes the product suite extremely versatile.  Whether you are pressed for time and need to analyze your code while in visual studio, you prefer a standalone GUI, or you are addicted to the command line, this product is made to fit your needs. Installation The NDpend installation process is very straight forward.  The download is a zip file that contains the complete product suite.  You simply pick a folder to install to and unzip the archive.  If you’ve purchased the pro version, you will be provided with a license in the form of an XML file which needs to be placed in the directory that you chose to install the product. Installing the Visual Studio 2012 add-in Once you’ve unzipped the archive, you need to run the NDepend.Install.VisualStudioAddin.exe executable to install the Visual Studio add-in. Running the install The installation completed Adding an NDepend project to your solution When you use the Visual Studio integration, you need to create an NDepend project in the solution that you wish to analyze. NDepend will tell you anything that wish you know about source code.  This is powerful, however, it’s a point that must be covered.  In order to be productive with NDepend, you must first define what information that you wish to discover about your source code and how you plan to use that information.  If you don’t have this information then you will not get much use from the product.  The information that it provides to you is very useful, however, you must take some time to plan out how you will use this information to benefit you and your coding efforts. You may wish to make sure that your code maintains a consistent amount of test coverage.  Perhaps you wish to make sure that all methods in your codebase stay below a certain threshold regarding the number of lines of code that they contain.  NDepend is capable of telling you this and much more about your source code. One of the coolest features that I’ve seen in the product is the Code Query Linq (CQLinqing).  This allows you to query your source code using LINQ syntax to bring back anything that you wish to know about your source code.   You can query an assembly, a class, even a method.  The product comes with predefined CQLinq rules but also allows you to create your own rules as well as edit existing rules. I plan to write another blog post that explains my personal experience with the product.  I’ve recently joined an open source project that is a framework that handles some very advanced topics such Artificial intelligence, Machine learning, and language design.  The project is called neural network designer .  I chose this project because the source code is vast and I believe that a large code base is a perfect target to use NDepend to get the most benefit. I plan to use the product and test the following areas:   What information do I want to know about my code base?   When do I wish to be presented with this information?   How do I plan on using this information to improve my code?   How can I use NDepend to provide this information? I think that if you wish to get any use out of the product, it will be very important that you answer these questions.  The product is vast and diverse but it can also be a bit intimidating.  With that said, I plan to use my next post to illustrate how I was able to use NDepend to define the metrics that I needed from my code, and how I used NDepend to provide those metrics to me. Stay tuned for the next installment which will explain my experience with using NDepend to improve my development efforts and my source code. Thanks for reading, Buddy James kick it on

About the author

My name is Buddy James.  I'm a Microsoft Certified Solutions Developer from the Nashville, TN area.  I'm a Software Engineer, an author, a blogger (, a mentor, a thought leader, a technologist, a data scientist, and a husband.  I enjoy working with design patterns, data mining, c#, WPF, Silverlight, WinRT, XAML, ASP.NET, python, CouchDB, RavenDB, Hadoop, Android(MonoDroid), iOS (MonoTouch), and Machine Learning. I love technology and I love to develop software, collect data, analyze the data, and learn from the data.  When I'm not coding,  I'm determined to make a difference in the world by using data and machine learning techniques. (follow me at @budbjames).  

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