Thu Jul 26 2018
Tools that help you in machine learning field
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. When it comes to training computers to act without being explicitly programmed there exist an abundance of tools from the field of Machine Learning. There are several tools/packages that recently became very popular. In this article, we are going to make a list of the best machine learning tools that designed to help you in the difficult matter. These tools can access more accessible technologies, including in later versions, and facilitated further work.
Let’s see the tools for machine learning
Torch
Torch is a scientific computing framework with wide support for machine learning algorithms that put GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. Torch has maximum flexibility and speed in building your scientific algorithms to making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio, and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.
Theano
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano has been powering large-scale computationally intensive scientific investigations since 2007.
Caffe
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. With Caffe, models, and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day.
Lime
Lime is an easy-to-use Python package that does this for you in a more intelligent way. Taking a constructed model as input, it runs a second "meta" approximator of the learned model, which approximates the behavior of the model for different inputs. The output is an explainer for the model, identifying which parts of any input helped the model reach a decision and which didn't.
H2O
H2O is an open source software tool, embedded with a machine learning platform for businesses and developers. It was designed by H2O.ai and is written in the Java, Python and R programming languages. The platform is built with the languages developers are familiar with in order to make it easy for them to apply machine learning and predictive analytics. H2O can also be used to analyse datasets in the cloud and Apache Hadoop file systems. It is available on Linux.
Accord.NET
Accord.NET is a machine learning framework that is combined with audio and image processing libraries written in C#. The framework is designed for developers to build applications such as pattern recognition, computer vision, computer audition (or machine listening) and signal processing for commercial use. It's divided into multiple libraries for users to choose from. These include scientific computing, signal and image processing and support libraries, with features like natural learning algorithms, real-time face detection and more.
Shogun
Shogun is the oldest tool for machine learning. But now it is going through a lot of development by a team of expert programmers. It was Created by Soeren Sonnenburg and Gunnar Raetsch in 1999 and was written in C++. This tool provides algorithms and data structures for all the problems of the machine. It can be used in many languages such as C++, Octave, Python, Java, Ruby, R, Matlab. It includes various features like regression, pre-processing, visualization, model selection strategies, one-time classification, multi-class classification.
Apache Mahout
Apache Mahout, being a free and open source project of the Apache Software Foundation, has a goal to develop free distributed or scalable machine learning algorithms for diverse areas like collaborative filtering, clustering, and classification. Mahout provides Java libraries and Java collections for various kinds of mathematical operations. Apache Mahout is implemented on top of Apache Hadoop using the MapReduce paradigm. Once Big Data is stored on the Hadoop Distributed File System (HDFS), Mahout provides the data science tools to automatically find meaningful patterns in these Big Data sets, turning this into ‘big information’ quickly and easily.
Scikit-Learn
Scikit-Learn leverages Python’s breadth by building on top of Python packages. It is an efficient tool for data analyzing and even data mining. Scikit-learning is easily accessible to everyone. It is an open source and is available under BSD license. It is developed by the team of professional developers and machine learning experts. It is built on SciPy, matplotlib, NumPy.
Nervana Neon
Nervana and Intel have joined forces to build the next generation of intelligent agents and applications and Neon is its open source Python-based machine learning library. Founded in 2014, Neon lets developers build, train and deploy deep learning technologies in the cloud. Neon has lots of video tutorials and a 'model zoo' which houses pre-trained algorithms and example scripts.
Jupyter Notebook
Jupyter Notebook is a collaborative tool for when users need to share documents. It’s free but extremely packed with features, supports 40 languages, including R, Scala, and Python, and even container platforms - Docker and Kubernetes.
TensorFlow
TensorFlow is a leading tool for both research and all types of machine learning tasks. It can be used on all devices and operating systems but it’s particularly rich in development tools for Android. TensorBoard is a tool for graphical representation of machine learning processes in TensorFlow. This visualization allows data scientists to see where model components are located and zoom in on any part of the schema. It also shows model development metrics, histograms, audio, text, and visual data, and more.
LIBSVM
LIBSVM and LIBLINEAR are two popular open source machine learning libraries, both developed at the National Taiwan University and both written in C++ though with a C API. LIBSVM implements the SMO algorithm for kernelized support vector machines (SVMs), supporting classification and regression. LIBLINEAR implements linear SVMs and logistic regression models trained using a coordinate descent algorithm.
ai-one
Claiming to be 'biologically inspired intelligence', ai-one lets developers create intelligent assistants within most software applications. ai-one's 'Analyst Toolbox' provides a document library, building agents and APIs for developers. Ai-one can essentially turn data into generalised rule sets, enabling lots of in-depth AI and machine learning structures.
Eclipse Deeplearning4j
Eclipse Deeplearning4j is an open-source deep-learning library for the Java Virtual Machine. It can serve as a DIY tool for Java, Scala and Clojure programmers working on Hadoop and other file systems. It allows developers to configure deep neural networks and is designed to be used in business environments on distributed GPUs and CPUs. The project was created by a San Francisco company called Skymind, which offers paid support, training and an enterprise distribution of Deeplearning4j.
Azure
Microsoft launched three new machine learning tools at its Ignite conference in September 2017: the Azure Machine Learning Experimentation service, the Azure Machine Learning Workbench, and the Azure Machine Learning Model Management service. These are aimed at developers wanting to build their own AI agents or build-upon existing models. Microsoft also launched a tool for non-developers to use AI functionality within their Microsoft Excel spreadsheets. Microsoft provides three AI tools for developers: Custom Speech Service, Content Moderator, and Bing Speech APIs in an attempt to make AI 'accessible for all'.
Protege
Protégé’s plug-in architecture can be adapted to build both simple and complex ontology-based applications. Developers can integrate the output of Protégé with rule systems or other problem solvers to construct a wide range of intelligent systems. Most important, the Stanford team and the vast Protégé community are here to help.
OpenNN
OpenNN is a C++ programming library for experience developers implementing neural networks. It includes plenty of documentation and tutorials including an introduction to neural networks, although OpenNN is aimed at developers with lots of experience with artificial intelligence. OpenNN has also built a tool for advanced analytics called Neural Designer, which aims to simplify and interpret data entries by creating visual content such as graphs and tables.
Spark MLlib
Spark MLlib is an in-memory data processing framework. It features a large database of algorithms focusing on classification, regression, clustering and collaborative filtering. Within the Apache incubator, there is also an open source framework called Singa which provides a programming tool for deep learning networks across numerous machines.
Lastly say, it cannot be possible to mention all the existing tools for machine learning into one article, even the most popular. These are the tools that make your life easier for machine learning. If you know about some other great tools, then share with us in the comments section. Thank you!