- Feb 1, 2016
In the last year, we saw Machine Learning going main-stream. The approach is much more enveloped as it comes from computational learning theory. It is unlike a cloud environment, it is even more efficient and effective as it runs on GPU hardware alone. Moreover, in the last year there were many frameworks introduced for machine learning as a result of its increasing demand. But what is crucial is how these frameworks are being designed specifically to work with hardest parts of machine learning where the brilliant techniques are being introduced every now and then to cater to varied developers indulging in machine learning.
But for many of us are not even aware of this new concept trending in technology industry. So let us first have a comprehension of what it is really about?
What is Machine Learning?
According to the Wikipedia definition: “Machine Learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. It explores the study and construction of algorithms that can learn from and make predictions on data.”
It is a method of data analysis that automates analytical model building using algorithms that iteratively learn from data where it also allows computers to find hidden insights without being explicitly programmed where to look. It gives the computers the ability to learn without being explicitly programmed.
Why Machine Learning is Trending?
There are many factors due to which we see such a trend where data mining and Bayesian analysis top the list. Also, aspects like growing volumes and varieties of available data, computational processing and affordable storage have led to increase in exploring machine learning and taking it to a level where it evolves for computers to perform better.
Top 5 Frameworks for Machine Learning –
1. Microsoft Azure ML Studio
This machine learning tool is undoubtedly one of the best tools for making things happen. Azure Machine Learning (ML) Studio is a collaborative, drag and drop tools to build, test and deploy analytics solutions on your data. It provides you with a interactive and visual workspace to easily build or iterate on a predictive analysis model.
An overview of its capabilities: https://azure.microsoft.com/en-in/documentation/articles/machine-learning-studio-overview-diagram/
With the help of this tool, you can drag and drop datasets and run analysis of modules onto an interactive canvas connecting them to form an experiment. You can edit, save or run it again also and then convert the experiment into a predictive experiment before you publish it as a web service. It is very convenient and very robust. You don’t require programming; all you need to do is visually connect datasets and modules to construct your predictive analysis model.
Explore Azure Machine Learning for Free, to subscribe for free, click here: https://studio.azureml.net/?selectAccess=true&o=2
2. Amazon Machine Learning
This tool allows predictive analysis to be conducted through an experiment. It is extremely easy to develop with these tools by Amazon, also considered to be one of the most preferred frameworks for machine learning. It provides with amazing visualization tools and wizards that guide you through the process of creating machine learning models where you do not need to know complex ML algorithms and technology. Also, it is easier to obtain predictions for your applications using simple APIs where you can develop or manage any infrastructure without implementing custom prediction generation code.
Subsribe for free – Amazon Web Services: https://goo.gl/H0W0n1
It is one of the best frameworks for machine learning as it is highly scalable and can generate billions of predictions daily and serve those predictions in real time. There is not investment in hardware or software, you only pay once you start where Amazon Machine Learning tools are highly cost effective and efficient backed with an image of dependable technology. Take a look at one of the projects done with Amazon Machine Learning: http://aws.amazon.com/solutions/case-studies/buildfax-and-amazon-machine-learning/
3. Google TensorFlow
TensorFlow – An open source software library by Google for Machine Learning. The platform is not just another framework library, it helps you express computation as a data flow graph. They provide with useful tools to assemble subgraphs common in neutral networks and also allows users to write their own libraries. It offers a very flexible architecture which helps in easy deployment computation to one or more CPUs or GPUs in a desktop, server or mobile device with single API. You can run it on CPUs, GPUs, desktops, mobile computing platforms or even servers. You can scale and train your model to work faster on GPUs without having to code for it or change the code. Thus, making the platform very portable!
With this tool, you can also conduct research or form requirements, rewrite while you are machine learning. It allows researchers to push ideas to products and allows academicians to share code with greater productivity. It offers auto differentiation where you can define architecture and prepare a predictive model along with language options. All in all, it proves to be most robust open source platform for Machine Learning.
4. IBM Watson
This tool claims to analyze unstructured data. According to a survey by IBM Watson: 80% of the data is unstructured (This includes new articles, research, reports, social media posts and enterprise system data). The best part is that it uses natural language processing to understand grammar and context. It offers understanding of complex questions where it evaluates all possible meanings and thereby determines conclusions. It is a platform that offers answers and solutions based on supporting evidence and quality of information that is found.
It is automatically updated if you load new information. You can load all kinds of materials on Watson like word docs, PDFs and web pages. Known for its scaling expertise, Watson proves to be a very efficient tool for machine learning capabilities. It uses machine learning tools to conduct the best research. The prototype of Watson analytics seeks to abstract away data science, taking natural language queries and answering them based on the content of datasets.
The biggies have it all when it comes to Machine Learning, but there are other frameworks also making news. Among them are Caffe and Apche Singa where both stand in competition. Getting to the point, Caffe is a deep learning framework created keeping in mind the expression, speed and modularity. It expressive quality of the architecture makes it very robust as it encourages application and innovation. Without hardcore coding, you can define models and optimizations by configuration. Contact software development experts for such configuration, click here: http://www.heliossolutions.in/contact-us/
Also, extensible codes will allow active development for developers. Because of its high speed in development process and high scalability, it offers easy deployment for research and industry experiments.
Now, if you have are deciding on using machine learning tools, you know which ones are the best and offer you what. In case, you need more insights on the same, then you can get in touch with our software development experts at Helios Solution (IT Outsourcing Company India) who are special team of machine learning developers. This blog is contributed by them in order to spread awareness about machine learning as a technology concept. We will be coming up with more such updates, until then Stay Nerdy!