Introduction to Machine Learning Frameworks
Machine learning framework has been defined as a tool, library or an interface that gives developers the ease of creating the machine learning models. Machine learning framework provides a standard way that is used by the developers while deploying these applications as the user can selectively change the generic functionality of the frameworks by their application code and thus follows the standard way of development of code.
Top 10 Different Machine Learning Frameworks
So, now let’s look into ten different machine learning frameworks:
- Scikit-Learn: It is a free machine learning library that is built on SciPy (scientific python). It is used very extensively by Python Programmers. It was developed by David Cournapeau. You can do feature engineering with your data (increasing the number of features), scaling, pre-processing, splitting your data into training and test subsets. It also includes many machine learning algorithms like Linear Regression, Logistic regression, K–mean algorithm, support vector machines. It is very popular because it can easily work with NumPy and SciPy.
- Tensor Flow: It is also an open-source library that is generally used for deep learning or machine learning algorithms using neural networks. It is created by Google. Tensor Flow is a library for data flow programming, It uses various optimization techniques for calculation of the mathematical expression which is used to get the desired results. The salient feature of sci-kit learn are:
1. It works great with a mathematical expression that involves multidimensional arrays.
2. It is highly scalable across machines.
3. It works with a wide variety of data sets.
These features make it a very useful framework for deploying production models.
- Amazon Machine Learning: As the name suggests, it is provided by Amazon. It is a service that can be used by developers to create models. It can be used as a visualization tool and can be used by machine learning engineers to create models without having to know the very detail of every model. It can run or create all kinds of models like Binary classification, multi-class classification ensemble algorithms, regression models.
- Azure ML Studio: This framework comes from Microsoft. So how it works is that it allows registered Azure users to create and train models and after having done that you can use them as APIs to be consumed by other services. Users get up to 10GB of storage per account. It supports a wide variety of machine learning algorithms. One very good feature about this that even if you do not have an account, you can try out the service by logging in to the account anonymously and you can use ML studio up to 8 hours.
- MLib (Spark): It is Apache Spark’s machine learning product. It contains or supports all types of machine learning algorithms and utilities like regression classification (binary and multi-class), clustering, ensemble and many more.
- Torch: It is a scientific machine learning framework that supports various machine learning utilities and algorithms. The salient feature of this framework is that it puts GPU first. It has community-driven packages in machine learning, computer vision, image processing, deep learning and many more. It’s main is to provide high scalability, flexibility, and speed while creating machine learning models. It is definitely a framework to look for while building machine learning models.
- Theano: It is built using python. It allows us to define, create and optimize mathematical calculations. Like Torch, It can also use GPU which helps in optimization and scalability.
- Veles: It is written in C++ and it is a deep learning framework. Though it is written in C++, It does use python to perform automation. It is mainly used in neural networks like CNN(convolution Neural Networks) recurrent neural networks.
- H20: The name sounds interesting but this framework allows us to apply maths and predictive analytics to solve today’s problems. It uses some combines some cool features like:
1. Best of Breed Open Source Technology.
2. Easy to use WebUI.
3. Data Agnostic Support for all common databases.
Along with using H2o, we can work on with existing languages and also extend it seamlessly with Hadoop.
- Caffe: It is a deep learning framework that was created using speed, modularity in mind. It is mainly used with neural network problems and was founded by Berkeley Vision and Learning Center.
So, after getting to know some of the best frameworks out of many. Let’s conclude now.
Every field today produces data and data needs to be analyzed and modeled using certain algorithms so that it can be used to produce better future results. So, in short, that’s what machine learning does. It is an essential skill of the 21st century and most of the frameworks are open-source with developer communities. It is one of the growing fields in technology and the IT field.
This has been a guide to Machine Learning Frameworks. Here we have discussed the Top 10 Different Machine Learning Frameworks. You may also look at the following article to learn more –