– LSMs take the temporal aspect of the input into account
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Concept
Liquid State Machine
Reservoir/ Liquid
– large accumulation of recurrent interacting nodes → is stimulated by the input layer – Liquid itself is not trained, but randomly constructed with the help of heuristics – Loops cause a short-term memory effect – preferably a Spiking Neural Network (SNNs) → are closer to biological neural networks than the multilayer Perceptron → can be any type of network that has sufficient internal dynamics
Running State
→ will be extracted by the readout function
– depend on the input streams they’ve been presented
Readout Function
– converts the high-dimensional state into the output
– since the readout function is separated from the liquid, several readout functions can be used with the same liquid
→ so different tasks can be performed with the same input
A perceptron is a simple binary classification algorithm modeled after the biological neuron and is thus a very simple learning machine. The output function here is determined by the weighting of the inputs and by the thresholds. Perceptrons are used for machine learning as well as for artificial intelligence (AI) applications. If you don’t know the difference between AI, neural networks and machine learning you should read our article on the subject.
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What does the learning process look like?
A set of input signals are decomposed into a binary output decision, i.e. zeros or ones. By training with certain input patterns, similar patterns can thus be found in a data set to be analyzed. The following figure shows this learning process schematically.
Perceptron Learning Process
If a set threshold is exceeded or not reached by weighting all inputs, the state of the neuron output changes. If one now trains a perceptron with given data patterns, the weighting of the inputs changes. The perceptron thus has the ability to learn and solve complex problems by adjusting the weights.
However, a basic requirement to obtain valid results is that the data must be linearly separable.
What are Multilayer Perceptrons (MLP)
A multilayer perceptron corresponds to what is known as a neural network. Perceptrons thus form the neuronal base, which are interconnected in different layers.
The figure below shows a simple three-layer MLP. Each line here represents a different output.
three-layer MLP
However, neurons of the same layer have no connections to each other. For each signal, the perceptron uses different weights and the output of a neuron is the input vector of a neuron of the next layer. The diversity of classification possibilities increases with the number of layers.
Recurrent Neural Networks vs Feed-Forward Networks
Basically, neural networks are distinguished according to the recurrent and the feed-forward principle.
Recurrent Neural Networks
In the recurrent neural network the neurons are connected to neurons of the same or a preceding layer. Here, a basic distinction is made between three types of feedback. With the direct feedback the own output of a neuron is used as further input. In indirect feedback, on the other hand, the output of a neuron is connected to a neuron of the preceding layers. In the last feedback principle, lateral feedback, the output of a neuron is connected to another neuron of the same layer.
Feed-Forward Networks
In feed-forward networks, on the other hand, the outputs are connected only to the inputs of a subsequent layer. These can be fully connected, then the neurons of a layer are connected to all neurons of the directly following layer. Or short-cuts are formed. Some neurons are then not connected to all neurons of the next layer.
In almost no scientific discipline you can get around the programming language Python nowadays. With it, powerful algorithms can be applied to large amounts of data in a performant way. Open source libraries and frameworks enable the simple implementation of mathematical methods and data transports.
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What is scikit-learn?
One of the most popular Python libraries is scikit-learn. It can be used to implement both supervised and unsupervised machine learning algorithms. scikit-learn primarily offers ready-made solutions for data mining, preprocessing and data analysis. The library is based on the SciPy Toolkit (SciKit) and makes extensive use of NumPy for high performance linear algebra and array operations. If you don’t know what NumPy is, check out our article on the popular Python library. The library was first released in 2007 and since then it is constantly extended and optimized by a very active community. The library was written primarily in Python and is based on Cython only for some high-level operations. This makes the library easy to integrate into Python applications.
scikit-learn Features
Easily implement many machine learning algorithms with scikit-learn. Both supervised and unsupervised machine learning are supported. If you don’t know what the difference is between the two machine learning categories, check out this article from us on the topic. The figure below lists all the algorithms provided by the library.
machine learning algorithms provided by scikit-learn..
scikit-learn thus offers rich capabilities to recognize patterns and data relationships in a dataset. Thus, high dimensions can be reduced to visualize the relationships without sacrificing much information. Features can be extracted and data clustering algorithms can be easily created.
Dependencies
scikit-learn is powerful and versatile. However, the library does not exist completely solitary. Besides the obvious dependency on Python, the library requires the import of other libraries for special operations.
NumPy allows easy handling of vectors, matrices or generally large multidimensional arrays. SciPy complements these functions with useful features like minimization, regression or the Fourier transform. With joblib Python functions can be built as lightweighted pipeline jobs and with threadpoolctl methods can be coordinated as threads to save resources.
== Open source Python library – a collection of mathematical algorithms and convenience functions
– is mainly used by scientists, analysts and engineers for scientific computing, visualization and related activities
– Initial Realease: 2006; Stable Release: 2020 – depends on the NumPy module → basic data structure used by SciPy is a N-dimensional array provided by NumPy
Benefits
Features
– SciPy library provides many user-friendly and efficient numerical routines:
Available sub-packages
SciPy ecosystem
– scienitific computing in Python builds upon a small core of open-source software for mathematics, science and engineering
SciPy Core Software
More relevant Packages
– the SciPy ecosystem includes, based on the core properties, other specialized tools
The product and further information can be found here:
Apache Mahout is a powerful machine learning tool that comes with a seamless compatibility to the strong big data management frameworks from the Apache universe. In this article, we will explain the functionalities and show you the possibilities that the Apache environment offers.
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What is Machine Learning?
Machine learning algorithms provide lots of tools for analyzing large unknown data sets. The art of data science is to extract the maximum amount of information depending on the data set by using the right method. Are there patterns in the high-dimensional data relationships, and how can they be represented in a low-dimensional way without much loss of information?
Fields of machine learning
There is often a similar amount of information in the failure as when an algorithm was able to successfully create groupings. It is important to understand the mathematical approaches behind the tools in order to draw conclusions about why an algorithm did not work. If you don’t know the basic machine learning categories, it’s best to read our article on the subject first.
Machine Learning and Linear Algebra
Most machine learning methods are based on linear algebra. This mathematical subfield deals with linear transformations, vector spaces and linear mappings between them. The knowledge of the regularities is the key to the correct understanding of machine learning algorithms.
What is Apache Mahout
Apache Mahout is an open source machine learning project that builds implementations of scalable machine learning algorithms with a focus on linear algebra. If you’re not sure what Apache is, check out this article. Here we introduce you to the project and its main projects once.
Mahout was already released in 2009 and since then it is constantly extended and kept up-to-date by a very active community. Originally, it contained scalable algorithms closely related to Apache Hadoop and MapReduce. However, Mahout has since evolved into a backend independent environment. That is, it operates on non-Hadoop clusters or single nodes.
Features
The math library is based on Scala and provides an R-like Domain Specific Language (DSL). Mahout is usable for Big Data applications and statistical computing. The figure below lists all machine learning algorithms currently offered by Mahout.
Implemented mathematical functions and algorithms
The algorithms are scalable and cover both supervised and unsupervised machine learning methods, such as clustering algorithms.
Apache Mahout covers a large part of the usual machine learning tools. This means that data can be analyzed without having to change frameworks. This is a big plus for maintaining compatibility in the application.
Apache Ecosystem
The framework integrates seamlessly into the Apache Ecosystem. This means that an application can access the entire power of the data processing platforms and build very high-performance big data pipelines. The following figure shows the Apache data management ecosystem.
Apache Mahout ecosystem
Through connectivity to Apache Flink, stream data analysis pipelines can be built, or with Hive data from relational databases can be automatically converted into MapReduce or Tez or Spark jobs.
What role does xml play in Industry 4.0? – XML is one of the most popular and widely used data formats. Its widespread use is also its most important advantage. XML is interpretable by both humans and machines and is therefore widely used to import and export application data. XML stands for Extensible Markup Language and is a markup language for representing hierarchically structured data in text file format. It was already published in 1998 and is primarily a meta language.
That means that on its basis application-specific languages are defined by structural and content restrictions. For example RSS, MathML, GraphML, but also the Scalable Vector Graphics (SVG). All web browsers are able to visualize XML documents using the built-in XML parser.
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What is XML Document Structure
An XML document can always be described as the interaction of its main components. In addition to the data itself, these are the layout, i.e. the description of the relationships between individual containers, and the structure.
What role does xml play in Industry 4.0? – Document Components
An XML structure can be interpreted as a tree. Thus, each XML document has a root element and texts or attributes as sub-elements.
An XML document can have an optional header in addition to the actual data. XML declarations, i.e. references to an external document type definition (DTD), or internal DTD, or document type declarations can be placed here. Examples for these declarations are the XML version or the encoding.
Classification of the XML format
The XML format can be further classified. Which class comes into question when is determined by the use case. Mainly we decide between document-centered and data-centered. The document-centric XML format is based on a text document and is difficult to process by machine due to its weak structure. In data-centric, the schema describes entities of a data model and their relationships. This format is optimized for efficient processing by machines. The Semistructured format represents a hybrid of both.
Processing
The XML format allows both sequential and optional accesses. This can be done either by a “push”, where the program flow is controlled by the parser, or by a “pull”, where the flow is implemented in the code that calls the parser. Management of the tree structure can be hierarchical as well as nested.
XML-Schema vs. Database-Schema
Besides XML, JSON is also a very popular markup language. In this article we have recorded the most important information about this format.
Another large field of computer languages, i.e. formal languages developed for interaction between humans and computers, is occupied by database languages. They describe the structure of a database. Here, too, the data is organized as a plan.
If you want to know more about database language, read our article on SQL and NoSQL. Here we explain the most important differences.
But how does this schema differ from an XML schema?
What role does xml play in Industry 4.0?- Differences between an XML schema and a database schema.
XML contains nested elements with an unlimited nesting depth. To transfer this nesting to a database schema, the nested elements must be decomposed and linked by foreign key relationships. In XML format, the elements within an element can be repeated as often as desired. Elements of a given type do not always have to contain the same child elements. However, the order of elements is an integral part of the document structure. In a database schema, each column is always present only once and contains simple values. Therefore, if multiple elements are to be stored, another table must then be created. The order in which the values are stored is not important, unlike in XML.
– Open source stream processor framework developed by the Apache Software Foundation (2016) – Data streams with high data volume can be processed and analyzed with low delay and high speed
Flink provides various tools for efficient real-time processing of continuous data streams and batch data
Core functions
– diverse, specialized APIs: → DataStream API (Stream Processing) → ProcessFunctions (control of states and time; event states can be saved and timers can be added for future calculations) → Table API → SQL API → provides a rich set of connectors to various storage systems such as Kafka, Kinesis, Kubernetes, YARN, HDFS, Elasticsearch, and JDBC database systems → REST API
Stream Processing
How to handle this flood of data?
== Data is processed continuously with a short delay → without intermediate storage of the data in separate databases – several data streams can be processed in parallel – Each stream can be used to derive own follow-up actions and analyses
Architecture
Data can be processed as unbounded or bounded streams:
Unbounded stream
have a start but no defined end
must be continuously processed
Bounded stream
have a defined start and end
can be processed by ingesting all data before performing any computations(== batch processing)
– Flink automatically identifies the required resources based on the application’s configured parallelism and requests them from the resource manager.
–In case of a failure, Flink replaces the failed container by requesting new resources.
– Stateful Flink applications are optimized for local state access
PyTorch BigGraph – The graph is a data structure that can be used to clearly represent relationships between data objects as nodes and edges. These structures can contain billions of nodes and edges in an industrial context.
Typical Graph structure
So how can the multidimensional data relationships be accessed in a meaningful way? Graph embedding offers one possibility for dimension reduction. This is a sequence of different algorithms with the goal of reducing the graph’s property relations to vector spaces. These embedding methods usually run unsupervised. If there is a large property similarity, two points should also be close to each other in the vector space.
The reduced feature information can then be further processed with additional machine learning algorithms.
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What is PyTorch BigGraph?
Facebook offers PyTorch BigGraph, an open source library that can be used to create very performant graph embeddings for extremely large graphs.
PyTorch BigGraph Principle
It is a distributed system that can unsupervised learn graph embeddings for graphs with billions of nodes and trillions of edges. It was launched in 2019 and is written entirely in Python. This ensures absolute compatibility with common Python data processing libraries, such as NumPy, Pandas, and scikit-learn. All calculations are performed on the CPU, which should play a decisive role in the hardware selection. A lot of memory is mandatory. It should also be noted that PBG can process very performant large graphs, but is not optimized for small graphs, i.e. structures with less than 100.000 nodes.
Facebook extends the ecosystem of its popular Python scientific computing package PyTorch with a very performant Big Graph solution. If you want to know more about PyTorch, you should read this article from us. Here we will show you the most important features and compare it with the industry’s top performer Google Tensorflow.
Fundamental building blocks
PGB provides some basic building blocks to handle the complexity of the graph. The graph partitioning splits the graph into equal parts and can be processed in parallel. PGB also supports multithreading computations. A process is divided into several threads, which run independently, but can access the same memory. In addition to the distribution of tasks, PyTorch BigGraph can also be used intelligently by distributed execution of hardware resources.
PyTorch BigGraph- How does the training work?
The PGB graph processing algorithms can process the graph in parallel using the fundamental building blocks already described. This allows the training mechanisms to run in a distributed manner and thus with high performance.
Once the nodes and edges are partitioned, the training can be performed for one bucket at a time.
PyTorch BigGraph – Parallel Training through Graph Partitioning
The training runs unsupervised on an input graph by reading its edge list. A feature vector is then output for each entity. Here, neighboring entities in the vector space are placed close to each other, while unconnected entities are pushed apart. Thus, the dimensions are iteratively reduced. It is also possible to configure and optimize this calculation using parameters learned during training.
PGB and Machine Learning
The graph structure is a very information-rich and so far unfortunately too much neglected data structure. With tools like PGB the large structure is more and more equalized by high parallelism.
A very interesting concept is the use of PGB in machine learning large graph structures. Here, the graph structures could be used for semantic queries with nodes, edges and properties to represent and store data and could replace a labeled data structure. Through the connections between the nodes certain relations can be derived. By PGB the graph can be processed enormously parallelized. This would allow individual machines to train a model in parallel with different buckets, using a lock server.
In times of Big Data, the graph has become a popular data structure due to its flexible and clear relationship-based structure. Even entire database systems are now designed according to the graph principle. For more on this, read our article on NoSQL databases. Libraries, like PyGraph, allow you to perform fast queries and optimized graph manipulations. With its full Python implementation, it offers you a user-friendly and powerful tool.
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What is a graph?
In a graph, objects are represented according to their relationships with each other. The objects are called vertices and the relations are called edges of the graph. An edge always connects exactly two nodes. Graphs are often used to represent traffic networks, entity-relationship diagrams, syntax trees for programming languages, finite automata and proof or decision trees.
Schematic representation of a graph structure and its components
PyGraph supports different graph types
Basically, graphs must be differentiated between directed and undirected. If a graph is directed, the edges may only be used in one direction. These edges are also called directed edges. If it is undirected, there are no directional constraints. So each edge is connected to an undirected pair of vertices. In the following figure we have contrasted both categories.
Comparison of undirected and directed graphs
You can use PyGraph regardless of these properties, because both types are supported.
PyGraph supports several algorithms
PyGraph supports the use of many well-known graph operations. For example, searching or traversing a graph, where all nodes of a graph must be visited, can be done in different ways. In the Depth-First Search (DFS) search algorithm, for example, the successors of a successor of the current node are visited first and only then the neighbors of the current node.
The depth of the search can also be limited accordingly. Breadth-First Search (BFS), on the other hand, first visits its own neighboring nodes and only then the successors of the neighboring nodes.
In addition to the algorithm-based search of a graph, other operations can be performed with PyGraph, such as the calculation of minimum spanning trees. This tree describes the best possible path to traverse all available nodes in a weighted graph. In the following figure we have shown you all currently supported algorithms.
