Currently, the hottest buzzword in business and computing is artificial intelligence. The real cutting edge is machine learning. If you seek to expand your horizons in the IT sector or incorporate technology to propel your business, it is critical to understand how machine learning works. It will be advantageous to your business in the near future.

Learning is a process of enhancing your ability to execute a given task professionally. Machine learning is a sub-field of computing and informatics which enables contemporary computers to learn without being programmed explicitly.

Machine learning has evolved from artificial intelligence, through learning theory and pattern recognition. It uses algorithms which can help in giving accurate data predictions. Developers have applied machine learning in an array of computing tasks, where designing operational programs and algorithms is a nightmare. Such areas include search engine improvement, website design, data mining, email spam filtering, digital image filtering and optical character recognition among others. According to Tom M. Mitchell, a famous professor and computer scientist at Carnegie Mellon University, machine learning falls into three categories, subject to the feedback and learning signal available to any learning system. The three categories are:

  • Reinforcement learning: a field of machine learning that explains how software agents act in certain environments to increase a notion of collective reward. Developers apply reinforcement learning in diverse areas such as information theory, swarm intelligence, genetic and statistics algorithms and game theory. A machine learning environment takes the form of a Markov decision process (MDP) owing to the dynamic programming techniques.

  • Unsupervised learning: a machine learning task that helps to infer certain functions that describe concealed structures in unlabelled data. It has a close association to density estimation in mathematical statistics.

  • Supervised learning: a machine learning task that infers functions from training data that has labels. Supervised learning has a pair that consist of supervisory signal (desired output value), and vector (input object).

Machine learning has diverse areas of application and it is gradually becoming popular, not only due to powerful and cheap hardware, but also because of the readily available open source and free software that enables machine learning to work easily. Machine learning researchers and practitioners, who are part of the larger website design and software engineering team, make sophisticated products through incorporating intelligent algorithms with the end product to enhance the operations of the software. Read on to understand various machine learning tools that help engineers to maintain, implement and build machine learning systems and develop new projects.

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1. Apache Singa

Gunnar Raetsch and Soeren Soonenburg are the brains behind this auspicious machine learning tool. It is currently under development with a large team of website designers and programmers. It is an open and free source toolbox written in C++ that provides data structures and algorithms for machine learning problems. Shogun Toolbox enables the use of a toolbox through an interface from Octave, C++, R, Java, Python, and Lua, and it can run on MacOS, Linux, and Windows. It is made for large-scale machine learning for a broad range of learning settings and features such as regression, clustering, classification and dimensionality reduction. It has an array of state-of-the art algorithms that include multiple kernel learning, Krylov methods, SVM implementations and kernel hypothesis testing. Shogun is compatible with other machine learning libraries such as SVMLight, libqp, Tapkee, GPML, LibSVM, LibLinear, SLEP and VowpalWabbit. Shogun has many features which include structured output learning, in-built model selection strategies, multi-class classification, visualization and test frameworks, large scale learning, and regression among others. Its latest version is 4.1.0.

2. Apache Mahout

It is an open and free source project from Apache Software Foundation, and its main aim is to develop scalable and freely distributable machine learning algorithms for use in various areas such as classification, clustering and collaborative filtering. Apache Mahout enhances website design by providing Java collections and Java library’s numerous mathematical operations. For a designer to implement Apache Mahout, they should have Apache Hadoop and MapReduce paradigm. Mahout avails data science tools that cause meaningful patterns when Hadoop Distributed File System (HDFS) has stored data. Canopy, Dirichlet, Mean-Shift and k-Means are some of the clustering algorithms that Mahout supports. If a designer needs to build a recommendation engine, Mahout has fast and flexible tools such as Taste Library.

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3. Apache Spark MLlib

A machine learning library whose main purpose is to make machine learning easy and scalable. It consists of renowned utilities and learning algorithms such as collaborative filtering, lower-level optimization, classification, regression, dimensionality reduction and clustering. Spark MLlib is a distributable machine learning framework embedded on Spark Core which enables it to distribute spark architecture at a faster speed than Apache Mahout. Some of the website design, statistical algorithms and machine learning algorithms that have been implemented via MLlib include:

  • Collaborative filtering techniques such as Alternating Least Squares (ALS)

  • Optimization algorithms that include limited-memory BGGS and stochastic gradient descent

  • Regression and classification which supports vector machines, naïve Bayes classification, linear regression and logistic regression

  • Summary statistics, random data generation, hypothesis testing and correlations.

 4. TensorFlow

Google Brain Team is the brain behind this open source software library that aids machine learning. TensorFlow is found active in conducting sophisticated research, language and perceptual understanding tasks on machine learning. It is the second machine learning system from Google Brain Team and it can function on various GPUs and CPUs. TensorFlow is applicable in various products such as Gmail, Google speech recognition, Search and Google Photos. It performs various numerical computations through data flow graphs. The graphs elaborate mathematical computations use edges and nodes, and this makes them easy to understand. Nodes execute mathematical operations, and are vital in representing push out results and feeding data. Edges explain the output/input relationships amongst various nodes. TensorFlow’s features include:

  • Portable: it can function on various GPUs and CPUs or mobile computing platforms. Also, it can run on cloud since it supports Docker.

  • Diverse language options: it has an easy interface, powered by Python. It enhances website design since writing code is easy, as well as using data flow graphs.

  • Highly flexible: TensorFlow helps users develop and execute their own code using Python and C++.

5. Oryx 2

It is a replica of Lambda architecture that is founded on Apache Kafka and Apache Spark for large-scale and real-time machine learning. Oryx 2 is mainly used in building applications and consists of end-to-end applications for clustering, regression, and collaborative filtering. It has three main tiers which are:

  • End-to-end implementation of similar standard machine learning algorithms as applications (k-means, random decision forests and ALS).

  • Specialization which helps to provide machine learning abstraction in hyper-parameter selection.

  • General Lambda architecture tier that provides serving and speed layers which are not exclusive to machine learning.

Oryx 2 has various Lambda architecture and connecting elements such as:

  • Serving layer that is responsible for receiving models, updating and implementing synchronous API, as well as revealing query questions on data results.

  • Batch layer that helps in computing new results based on previous results and historical data.

  • Data transport layer which is responsible for moving data among layers and taking input from other sources.

  • Speed layer helps in producing and publishing incremental updates of the model from an array of new data.

6. Accord.NET

It is used in scientific computing and it is part of the .NET machine learning frameworks. It has diverse libraries that are applicable in pattern recognition, artificial neural networks, statistical data processing, signal and image processing and linear algebra. It has various libraries such as Accord, Accord.Statistics, Accrod.Neuro, Accord.Audio, Accord.Controls and Accord.MachineLearning.

If you have more machine learning tools, share them in the comment section below.

Postado 18 novembro, 2017


Software Developer

Lucy is the Development & Programming Correspondent for She is currently based in Sydney.

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