What is H2O in Machine Learning? H2O.ai is a leader in the 2018 Gartner Magic Quadrant for Data Science and Machine Learning Platforms.
H2O is open-source software for big-data analysis. It is produced by the company H2O.ai. H2O allows users to fit thousands of potential models as part of discovering patterns in data.
The H2O software runs can be called from the statistical package R, Python, and other environments. It is used for exploring and analyzing datasets held in cloud computing systems and in the Apache Hadoop Distributed File System as well as in the conventional operating-systems Linux, macOS, and Microsoft Windows.
The H2O software is written in Java, Python, and R. Its graphical-user-interface is compatible with four browsers: Chrome, Safari, Firefox, and Internet Explorer.
H2O is a Java-based software for data modeling and general computing. The H2O software is many things, but the primary purpose of H2O is as a distributed (many machines), parallel (many CPUs), in memory (several hundred GBs Xmx) processing engines.
There are two levels of parallelism:
- within node - across (or between) nodes The goal of H2O is to allow simple horizontal scaling to a given problem in order to produce a solution faster. The conceptual paradigm MapReduce, along with a good concurrent application structure, enable this type of scaling in H2O.
Video for H2O https://www.youtube.com/watch?v=9W_c2Ec23PM
Linear regression is a linear system and the coefficients can be calculated analytically using linear algebra. ...
Linear regression does provide a useful exercise for learning stochastic gradient descent which is an important algorithm used for minimizing cost functions by machine learning algorithms.
Linear regression is a very simple approach for supervised learning. Though it may seem somewhat dull compared to some of the more modern algorithms, linear regression is still a useful and widely used statistical learning method. Linear regression is used to predict a quantitative response Y from the predictor variable X. Linear Regression is made with an assumption that there’s a linear relationship between X and Y.
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).
When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example.
Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.
It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach.
Accord.NET is a framework for scientific computing in .NET. The source code of the project is available under the terms of the Gnu Lesser Public License, version 2.1.
The framework comprises a set of libraries that are available in source code as well as via executable installers and NuGet packages.
The main areas covered include numerical linear algebra, numerical optimization, statistics, machine learning, artificial neural networks, signal and image processing, and support libraries (such as graph plotting and visualization). The project was originally created to extend the capabilities of the AForge.NET Framework, but has since incorporated AForge.NET inside itself. Newer releases have united both frameworks under the Accord.NET name.
The Accord.NET Framework has been featured in multiple books such as Mastering.NET Machine Learning by PACKT publishing and F# for Machine Learning Applications
After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible. It is based on the following pattern:
Choose a learning algorithm that provides a Learn(x, y) or Learn(x) method;
Use the Learn(x, y) to create a machine learning model learned from the data;
Use the model's Transform, Decide, Scores, Probabilities or LogLikelihoods methods.
For Installing process check here: https://github.com/accord-net/framework