The era of AI and machine is here, right now. Based on a report by Gartner, AI and machine learning projects to create nearly 2.3 million jobs by the end of 2020.
No wonder why this massive growth has led to the evolution of multiple frameworks that AI experts seek to learn.
Let us have a glance at the most common libraries and frameworks:
Azure ML Studio
This gives Microsoft Azure users create and train models and further turn them into APIs to be consumed by other services. These users ideally get 10GB storage count to model the data. If you’re looking to provide service to a larger model you can connect to your Azure storage. You can find a few algorithms available, courtesy goes both to Microsoft and other third parties.
Amazon machine learning
Amazon machine learning (AML) is a service that provides developers of every skill level to learn how to use machine learning. The AML also offers tools and technologies in guiding the individual how to create a machine learning model without the need to learn machine learning technology or algorithm. The machine learning model connects to the data stored in Redshift or Amazon S3 and can be run through classification, or regression methods that help build a model.
With the help of H2O, anybody can easily apply predictive analytics and math to solve the world’s most difficult challenges. It helps in combining features that cannot be easily found in machine learning platforms such as Easy-to-use WebUI, Data Agnostic Support for Common Database, or Best of Breed Open Source Technology. With the help of H2O, AI experts can easily work with existing tools.
MLlib (Spark) is one of the common Apache’s Spark machine learning library. This library is further used for making the practical machine easy and increases its scalability. This includes genuine learning algorithms such as clustering, regression, dimensionality reduction, collaborative filtering, and high-level pipeline APIs.
The pattern is classified as one of the web mining modules for Python programming which also includes tools for data mining – Wikipedia API, crawler, HTML DOM parser, natural language processing, network analysis and visualization, Twitter, and Google.
Shogun, created in 1999 is considered the oldest and venerable libraries in machine learning. Though it is written in C++, it is not limited to work in a C++ environment. Because of the SWIG library, Shogun can be used in programming languages such as Python, Java, Ruby, R, Lua, Matlab, and Octave. This library is specially designed for a broad range of learning settings such as classification, explorative data analysis, and regression methods.
Scikit-Learn is a library that helps in leveraging Python’s breadth. This is simply done by building on the existing libraries such as NumPy, Matplotlib, and SciPy to simply science and math work. It is an added advantage for an AI professional who is looking to pursue AI as their career. Scikit-learn uses tools and technologies to take on tasks such as classification, regression, and clustering.
The torch is well-known for its computing framework. It offers great efficiency and is easy to use. Thanks to the fast scripting language used i.e. LuaJIT. The core aim of this framework is to have high speed and flexibility while building specific algorithms.
This Python library is responsible for defining, optimizing and evaluating a mathematical expression, mostly used for the ones having multi-dimensional arrays. With the help of Theano, you can easily attain a speed that rival hand-crafted C implementation used for solving complex problems that involve a large amount of data.
A deep learning framework that is meant to have speed, expression, and modularity. Caffe was developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Because of its speed, this framework makes it perfect for industry deployment and research experiments.