Developers can use the experience of software giants like Google or Facebook to provide their own apps with artificial intelligence. The frameworks work together with the currently working development environments and programming languages. In most cases, no new knowledge is needed to make your apps more effective and intelligent.
Deep learning framework Caffe2
The deep-learning framework Caffe was originally developed at the University of California. The inventor is now employed on Facebook. He is responsible for the development of software for AI. Facebook is driving the development of Caffe forward. For the solution to have enough power, graphics processors from NVidia are used. The software is available as an open source system.
Caffe can be used, for example, for speech recognition, image recognition and classification or for the development of natural languages ??in AI devices. Those who want to experiment with artificial intelligence are in good hands with Caffe2. The developers provide templates for testing the insert. Caffe has interfaces to C ++ and Python. Caffe2 can also be used for neural networks and generally works with smartphones.
The software is also enormously important for Facebook, as the social network wants to focus more on Augmented Reality (AR) in the future . Using AR, the artificial world can be combined with the real world to create new kinds of programs that can interact with the real world.
scikit-learn – Machine Learning with Python
The scikit-learn library , derived from SciPy Toolkit, builds on the Python programming language. Packages like NumPy, SciPy, or Matplotlib are used by Scikit-learn to write mathematical, scientific, or statistical programs in Python. Scikit-learn can also be used for data mining and data analysis.
scikit-learn is available free of charge under the BSD license. Also this solution is able to create applications for artificial intelligence. One example is recognizing bots or developing apps for speech assistants and other artificial intelligence solutions. Scikit-learn can thus distinguish computer-generated messages on the Internet from human-made texts.
If you want to work with machine learning and artificial intelligence based on Python , you should take a look at the possibilities of Scikit-Learning. The vendors also provide several tutorials that developers can use to work with Python and scikit-learn. Through the active community and the elaborated documentation can quickly achieve results. Scikit-Learn also works with other packages, such as Pandas or TensorFlow.
The programs and functions written with scikit-learn can also be integrated into other programs. This allows programs to get AI functions that were not possible without scitkit-learn.
Machine learning with shogun
The machine-learning software ” Shogun ” is also one of the known solutions in the field of artificial intelligence. The library supports many languages, such as Python, Octave, R, Java / Scala, Lua, C #, and Ruby. This allows you to create scientific programs based on Linux / Unix, macOS and Windows . The solution does not depend on trends in programming languages ??and can be used flexibly with the language that is best suited to the respective object. A change is possible at any time, so that developers do not maneuver in a dead end, if the currently used programming language is less common in the future.
Shogun can help with regression analysis and classification problems. The focus is in bioinformatics. The system uses core methods and sequences, also called string cores. This allows large amounts of data to be analyzed in a short time. Shogun has interfaces to Support Vector Machines (SVM). These are learning models with associated learning algorithms that analyze data. SVMs are not machines, but mathematical methods for subdividing objects into classes.
These include SVMlight or libSVM. Shogun also runs as a container image. Shogun also works with other libraries. These include, in addition to SVMlight and libSCM, LibLinear, LibOCAS, libqp, VowpalWabbit, Tapkee, SLEP, GPML and more. The developers provide tutorials on your website to learn how to use the solution.
Accord.NET Framework is software for creating machine learning software. It also offers libraries for audio and image processing. The solution is the successor to AForge.NET. The framework is a .NET framework combined with audio and image processing libraries written entirely in C #.
The framework is for creating production-ready signal processing and statistical applications for commercial use. A collection of sample applications (http://accord-framework.net/samples.html) enable a quick start and get up and running quickly. A documentation and a wiki (https://github.com/accord-net/framework/wiki) help to get involved.
The framework offers a number of different libraries. These are available in source code, as executable installers, as well as NuGet packages. They can be downloaded directly from the NuGet source and used on the corresponding device. Accord.NET Framework supports numeric and linear algebra, numerical optimization, statistics and machine learning. Neural networks can also be implemented with the Accord.NET Framework.
Apache Mahout – Big Data Meets Machine Learning
Apache Mahout is a library of scalable machine learning algorithms based on Apache Hadoop and MapReduce. The advantage of the solution is that it works in big data environments. Apache Mahout allows you to work with Apache Hadoop. Statistical calculations can be performed.
Mahout is therefore an important open-source software when it comes to developing software in the field of artificial intelligence. The solution works with other big data products, such as Apache Spark. The interactive shell allows a direct connection to different apps. This is how to use a domain-specific language (DSL), which can be compared to R. If you know R, you can handle mahout quickly. Spark and speed can be used in parallel. Code that was written with the DSL for Sparking mostly with speed.
Mahout focuses on linear algebra. The distributed row matrix (distributed row matrix) can be used as a data type in mahout. Mahout is integrated into Apache Zeppelin . It is a solution that simplifies the collection and analysis of data from big data systems. Visualizations from ggplot, a plot system, and matplotlib, a program library for Python, can also be used in Mahout. The calculations can also be accelerated by graphics CPUs in the computer.
Spark MLlib is a machine learning library that lets Apache Spark make the most of machine learning with its other features. Spark MLlib can be used with Java, Scala, Python and R. MLlib uses the APIs of Spark and interacts with NumPy in Python. NumPy is a Python library that handles vectors, matrices, and multidimensional arrays. .
R libraries can also be used with Apache Spark and MLlib (from Spark 1.5). You can use any Hadoop data source to facilitate integration with Hadoop workflows. File systems such as HDFS, HBase and local files can be used in parallel. This allows data to be processed and shared for machine learning and data from big data environments.
The developers assume that the algorithms are sometimes a hundred times faster than MapReduce. Both systems are structured and unstructured at high speed. This is especially important for environments in the big data area. MLlib contains algorithms that better work with Apache Spark and thus provide better results than the One Pass approximations used in MapReduce.
The advantage of MLlib is that the library runs everywhere, including Spark, Hadoop, Apache Mesos and Kubernetes. Clusters can be used locally, but data in the cloud can also be used. MLlib so runs on clusters in the cloud, which in turn can use different data sources.
Spark can be operated with MLlib in its standalone cluster mode, on Amazon AWS (EC2), on Hadoop YARN, Mesos or with containers and Kubernetes. Data can be read from HDFS, Apache Cassandra, Apache HBase, Apache Hive and many other sources.
The H2O open source software combines machine learning capabilities with scalable in-memory big data processing . Machine learning can be used in combination with big data analytics. In H2O, the responsiveness of in-memory processing is combined with the ability to quickly serialize between nodes and clusters. H2O scales quickly and easily. The administration is currently carried out with a web-based flow GUI. POJOs can be deployed to obtain data for accurate predictions in any environment. A POJO is a Java object that has no limitations.
H2O can access HDFS directly, as well as data from Yarn, a big data analysis system, and MapReduce. H2O can also be started directly in Amazon AWS- EC2 instances. It can communicate with Java via Hadoop, but also Python, R and Scala can be used, including all supported packages.
Since H2O builds directly on HDFS, the solution achieves high performance when HDFS is used as a storage system. The KI framework H2O provides a set of algorithms to develop and manage AI, along with big data and machine learning. Examples are Deep Learning, Gradient Boosting and Generalized Linear Models. These are machine learning technologies with which, for example, regression analyzes can be carried out. Together with Apache Spark it can also be used for calculations and applications in the cloud . Insurance companies, for example, use H2O because complex calculations can be made here.
Oryx 2 – real-time machine learning
Oryx is software that uses data from Kafka and Spark. This allows data from big data analytics to be used for machine learning as well. Kafka and Spark are both big data systems. It is possible to perform machine learning in real time. Data can be processed in real time, from different sources.
Oryx 2 is based on the lambda architecture . This new type of data management is primarily used in big data environments. The architecture is therefore used with Apache Spark and Apache Kafka. The solution specializes in machine learning. The framework can be used to build applications, but also offers end-to-end applications for collaborative filtering, classifying, regression, and clustering.
Oryx 2 consists of three levels: The batch layer calculates historical data . This operation can take several hours and start several times a day. The velocity level creates and publishes the incremental model updates from a stream of new data . These updates can be on the order of seconds. The third level, the serving layer, receives models and updates. The data transport layer shifts data between layers and receives input from external sources.
Google DeepVariant is an AI gene sequencing software. The software can be operated in Google Cloud . The open source software is based on TensorFlow. It can use data from gene sequencing to calculate a genome. If the software is used in Google Cloud , you can book two models. Both use 1025 processor cores.
The faster model also uses graphic adapters from NVidia for the calculation. These can count on different speeds. Google DeepVariant makes very few mistakes when it comes to analyzing genes and can carry out genetic analyzes very quickly. Therefore, the software has also won prizes from the Food and Drug Administration (FDA). The AI-based software also works with neural networks.