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What Are Self Organizing Maps: Beginner’s Guide

What Are Self Organizing Maps: Beginner’s Guide

Updated on 15 March, 2023

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• 11 min read

Pavan Vadapalli

Table of Contents

What is a Self-Organizing Map?
Uses Of Self-Organizing Maps
Architecture and Functionality of Self-Organizing Maps
Advantages
Disadvantages
Implementing Self-Organizing Maps Using Python
Conclusion

Source

Do you ever feel like you’re swimming in data but don’t know how to understand it? Data is becoming more readable and less complex with rapid advances in data sciences, machine learning, and artificial intelligence.

Self organizing maps are an example of one such advancement that reduces the dimensionality of data to reveal correlations that would otherwise be difficult to decipher. Self organizing maps (SOMs) use an unsupervised learning approach to cluster and map data to unravel complex issues and problems. With machine learning expected to reach a two trillion dollar valuation by 2030, this is the right time to upskill and learn about SOM in machine learning.

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If you want a headstart in the right direction to understand the self organizing maps, you are in the right place. Read on to know more!

What is a Self-Organizing Map?

Self organizing maps (SOM) were introduced in the 1980s by the Finnish computer scientist Teuvo Kalevi Kohonen, also known as Kohonen’s Map after him. Self organizing maps are an example of Artificial Neural Networks that reduces data dimensionality through self-organising neural networks that support knowledge-based processing. Drawing inspiration from the structure and functioning of the human neural system, neural networks process and develop algorithms to untangle complex patterns, correlations, and problems.

Source

Self organizing maps are unsupervised neural networks trained through unsupervised and competitive learning algorithms. The networks develop their classifications without any external or specified target output. Hence, they are ‘self-organizing.’

The maps consist of two layers- the input layer and the output layer. By clustering and mapping, they take higher dimensional data sets and reduce them to a lower dimensional, discretised representation- usually two-dimensional- called a map. It helps simplify multidimensional, complex data while preserving the topological properties of the input layer.

Uses Of Self-Organizing Maps

Self organizing maps are the way of the future. The discretised representation of multidimensional training data simplifies complex issues. The critical function of transforming a higher dimensional dataset into a lower dimensional representation holds the key to uncomplicating training data. It makes data visualization easier for the human eye.

It does so without the threat of data loss from reducing training data into a lower dimensional output or dimensionality reduction. Unlike in Principal Component Analysis (PCA), self organizing maps have an advantage as they retain the topological or structural information of the training data lost in PCA. Therefore, in cases where all dimensions are essential, they are represented in the Kohonen map despite reducing the data into two-dimensional outer space.

Further, seismic facies analysis helps recognise and develop organized relational clusters or groups by identifying different individual features. Self organizing maps act as a calibration method that relates these clusters to physical reality in the absence of physical analogs.

Additionally, self organizing maps aid in text clustering. This critical preprocessing step enables verification of text to decipher how it can be converted to a mathematical expression through SOM and further analysed and processed. Moreover, SOM helps in exploratory data analysis by revealing underlying and hidden patterns, relationships, and groups within training data through clustering and visualisation.

Consequently, SOM in machine learning and artificial intelligence has many applications across fields- from pattern recognition, medical applications, telecommunications, robotics, product management, data mining and processing, and more!

Architecture and Functionality of Self-Organizing Maps

The architecture of self organizing maps is essential in understanding what they do and how they do it. SOMs consist of two layers of nodes- the input layer and the output layer (or the Kohonen layer or the SOM layer). The two layers are directly connected. The input layer consists of source nodes that express features, attributes, or variables. They are represented as m-dimensional input vectors, x = (x₁, x₂…xₘ). The output layer, or the Kohonen layer, has nodes arranged in topological architecture, which is usually two-dimensional with a grid organisation consisting of rows and columns.

Each node has a specific location in the grid, and each input vector has a corresponding weight vector, w = (w₁, w₂…wₘ). These nodes indicate the maximum number of clusters possible from the input data. The adjacency of nodes depicts similarity between clusters, and the distance between neighbours is unimportant. The map thus takes on different shapes, typically forming rectangular or hexagonal grids. Each topological structure has specific properties, and the hexagonal is the preferred version.

Now that we know the basic architecture of self-organising maps, let’s understand how they function. We will breakdown the functionality of SOMs into the following steps:

1. Initialisation of weights

The first step that initiates the mapping process in self organizing maps is the initialisation of weights to vectors. Random values are selected for the initial weight vectors (wₒ).

2. Sampling

A sample of the input training vector (x) is chosen randomly from the input space.

3. Similarity matching

Nodes compete to be activated and selected in this stage of the competition. The node whose weight vector is closest to the input vector becomes activated by computing their similarity using measurement methods. The most viable equation to measure distance [d₀ (t)] is the Euclidean distance for visual representation. The winning node is called the Best Matching Unit (BMU).

Source

4. Identify neighbourhood

In this step, the topological neighbourhood radius [nr(t)] of the BMU [c(t)] is identified. In this stage, the process of cooperation takes place.

5. Weight updating

It is the stage of adaptation in which the weight vectors of the BMU and nodes that fall within the neighbourhood in the output space are updated using the weight updation equation. It helps nodes in the output space closely resemble and represent the features of the input space. Two parameters are essential: learning rate [α(t)] and neighbourhood size.

6. Continuation

The process from step b onwards is repeated for N iterations till the feature map stops changing and takes on an identifiable shape.

Source: Example of how Self Organizing Maps work

Advantages

Self-organising maps are valuable in simplifying data to reveal the underlying patterns and relationships. There are several advantages of SOMs.

SOMs make complex and multidimensional data easy to understand and read because of reduced data dimensionality and clustering.
Self organizing maps do not cause data loss as the input data is preserved in the topological representation.
It retains the topological relations of the input space through clustering in outer space.
SOMs can negotiate an array of classification issues while providing a comprehensive and valuable summary at the same time.
They help in easier data visualisation.

Disadvantages

There are several advantages of self organizing maps, but they also have certain disadvantages.

SOMs require a large amount of good-quality training data.
The computational costs of self organizing maps are very high.
SOMs take a comparatively long time to prepare and train in the face of slowly evolving data.
They are not a good fit for categorical and mixed-type data.
The initial weight vector influences cluster patterns.
It is difficult to determine the optimal map size.

Implementing Self-Organizing Maps Using Python

Here is an example code for implementing a SOM using Python:

import numpy as np

from matplotlib import pyplot as plt

 

class SOM:

    def __init__(self, input_shape, output_shape, learning_rate=0.1, sigma=1.0):

        self.input_shape = input_shape

        self.output_shape = output_shape

        self.learning_rate = learning_rate

        self.sigma = sigma

        self.grid = np.random.randn(*output_shape, input_shape)

    

    def train(self, data, num_epochs):

        for epoch in range(num_epochs):

            for x in data:

                winner = self._find_winner(x)

                self._update_weights(x, winner)

    

    def _find_winner(self, x):

        x = np.expand_dims(x, axis=0)

        distances = np.linalg.norm(self.grid – x, axis=-1)

        return np.unravel_index(np.argmin(distances), self.output_shape)

    

    def _update_weights(self, x, winner):

        winner_weight = self.grid[winner]

        distances = np.linalg.norm(np.indices(self.output_shape) – np.array(winner)[:, np.newaxis, np.newaxis], axis=0)

        influence = np.exp(-distances ** 2 / (2 * self.sigma ** 2))

        self.grid += self.learning_rate * influence[…, np.newaxis] * (x – winner_weight)

    

    def get_map(self):

        return self.grid.reshape(-1, self.input_shape)

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Let’s go through this code step by step:

First, we import NumPy and Matplotlib, which we’ll use for numerical operations and visualisation, respectively.
Next, we define a class SOM to encapsulate our SOM implementation. The __init__ method takes three arguments: input_shape is the dimensionality of the input space, output_shape is the shape of the SOM grid, and learning_rate and sigma are hyperparameters for the learning rate and neighbourhood size, respectively. We initialise the grid of neurons randomly.
The train method takes two arguments: data is the input data to train the SOM on, and num_epochs is the number of training epochs. For each epoch, we iterate over each input vector in data and update the weights of the neurons using the _update_weights method.
The _find_winner method takes an input vector x and returns the neuron’s index in the grid with the closest weight vector to x. To do this, we compute the Euclidean distance between x and each neuron’s weight vector and return the index of the neuron with the smallest distance.
The _update_weights method takes an input vector x and the index of the winning neuron winner and updates the weight vectors of all neurons in the grid based on their distance to the winning neuron. We compute the Euclidean distance between each neuron’s index and winner and use this to compute an influence function that determines how much each neuron’s weight vector should be updated. The influence function is a Gaussian function of the distance, with a width controlled by the sigma hyperparameter. We update each neuron’s weight vector by adding a fraction of the difference between x and the winning neuron’s weight vector, scaled by the influence function and the learning rate.
Finally, the get_map method returns a flattened version of the grid, where each row corresponds to the weight vector of a neuron.

Here’s an example of how you can use this SOM implementation to cluster a dataset:

# Generate some sample data

data = np.random.randn(100, 2)

 

# Create a SOM with a 10×10 grid

som = SOM(input_shape=2, output_shape=(10, 10))

 

# Train the SOM for 100 epochs

som.train(data, num_epochs=100)

 

# Get the map of the SOM

map = som.get_map()

 

# Plot the data and the SOM

plt.scatter(data[:, 0], data[:, 1], color=’blue’)

plt.scatter(map[:, 0], map[:, 1], color=’red’)

plt.show()

In this example, we generate a dataset of 100 2-dimensional vectors using NumPy’s randn function. We create a SOM with a 10×10 grid and train it on the dataset for 100 epochs. Finally, we get the map of the SOM and plot it along with the original data using Matplotlib.

This should give you a good starting point for implementing SOMs in Python. The basic SOM algorithm has many variations and extensions, so feel free to experiment and explore!

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Conclusion

The immensity of available data today can make recognising the existing correlations, relationships, and patterns challenging. Converting this data into an easily readable and digestible model can be critical to forming actionable insights to solve real-world problems and issues. Self-organizing maps are an example of technological advancement that can benefit humanity. Clustering and mapping higher dimensional data into lower dimensional models while preserving all the topological properties make SOM in machine learning highly applicable across fields.

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Frequently Asked Questions (FAQs)

1. What are the three processes involved in training Self Organizing Maps?

The three processes involved in training self-organizing maps are competition, cooperation, and adaptation, which are part of the competitive learning process of the algorithm.

2. What is Competitive Learning?

SOMs differ from other artificial neural networks as they rely on competitive learning methods. Competitive learning can be defined as an artificial neural network learning process where different neurons or processing elements compete on who is allowed to learn to represent the current input.

3. What is Unsupervised Training in SOM?

Unsupervised training, or unsupervised machine learning, is the technique of analysing and clustering unlabeled datasets to identify hidden patterns and relations.

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The model needs to be checked for its predictability at different times and needs to be modified if its predictability reduces. For the banking employee to take an instant decision the moment a customer applies for a loan, the model needs to be integrated with the bank’s IT systems. The bank’s servers should host the model. As a customer applies for a loan, his variables must be captured from a website and utilised by the model running on the server. Then, this model should convey the decision — whether the credit can be granted or not — to the bank employee, instantly. This process comes under the domain of information technology, which is also utilised by data science. In the end, it is all about communicating the results from the analysis. Here, the presentation and storytelling skills are required to demonstrate the effects from the study efficiently. Design-thinking helps in visualising the results, and effectively tell the story from the analysis. Big Data The final piece of our puzzle is ‘Big Data’. How is it different from data science and machine learning? According to IBM, we create 2.5 Quintillion (2.5 × 1018) bytes of data every day! The amount of data which companies gather is so vast that it creates a large set of challenges regarding data acquisition, storage, analysis and visualisation. The problem is not entirely regarding the quantity of data that is available, but also its variety, veracity and velocity. All these challenges necessitated a new set of methods and techniques to deal with the same. Big data involves the four ‘V’s — Volume, Variety, Veracity, and Velocity — which differentiates it from conventional data. Volume: The amount of data involved here is so humongous, that it requires specialised infrastructure to acquire, store and analyse it. Distributed and parallel computing methods are employed to handle this volume of data. Variety: Data comes in various formats; structured or unstructured, etc. Structured means neatly arranged rows and columns. Unstructured means that it comes in the form of paragraphs, videos and images, etc. This kind of data also consists of a lot of information. Unstructured data requires different database systems than traditional RDBMS. Cassandra is one such database to manage unstructured data. Veracity: The presence of huge volumes of data will not lead to actionable insights. It needs to be correct for it to be meaningful. Extreme care needs to be taken to make sure that the data captured is accurate, and that the sanctity is maintained, as it increases in volume and variety. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Velocity: It refers to the speed at which the data is generated. 90% of data in today’s world was created in the last two years alone. However, this velocity of information generated is bringing its own set of challenges. For some businesses, real-time analysis is crucial. Any delay will reduce the value of the data and its analysis for business. Spark is one such platform which helps analyse streaming data. As time progresses, new ‘V’s get added to the definition of big data. But — volume, variety, veracity, and velocity — are the four essential constituents which differentiate data from big data. The algorithms which deal with big data, including machine learning algorithms, are optimised to leverage a different hardware infrastructure, that is utilised to handle big data. To summarise, Executive PG Programme in Data Science is an interdisciplinary field with an aim to derive actionable insights from data. Machine learning is a branch of artificial intelligence which is utilised by data science to teach the machines the ability to learn, without being explicitly programmed. Volume, variety, veracity, and velocity are the four important constituents which differentiate big data from conventional data.

by Thulasiram Gunipati

02 Nov'17

6.14K+

Natural Language Generation: Top Things You Need to Know

From a linguistic point of view, language was created for the survival of human beings. The effective communication helped a primitive man to hunt, gather and survive in groups. This means a language is necessary to carry out all activities needed for not only survival but also a meaningful existence of human beings. As humans evolved so did their literary skills. From pictorial scripts to well developed universal ones, we have made an impressive progress. In fact, such remarkable progress that a machine developed by humans now can read data, write text and not in a machine, binary language but a real, conversational language. Natural Language Generation has made this possible. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification What is Natural Language Generation? Natural language is an offshoot of Artificial Intelligence. It is a tool to automatically analyse data, interpret it, identify the important information and narrow it down to a simple text, to make decision making in business easier, faster and of course, cheaper. It crunches numbers and drafts a narrative for you. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. What are the different variations of Natural Language Generation? Basic Natural Language Generation: The basic form of NLG converts data into text through Excel-like functions. For example, a mail merge that restates numbers into a language. Templated Natural Language Generation: In this type of NGL tool, a user takes the call on designing content templates and interpreting the output. Templated systems are restricted in their capability to scan multiple data sources, perform advanced analytics. Advanced Natural Language Generation: It is the ‘smartest’ way of analysing data. It processes the data right from the beginning and separates it based on its significance for a particular audience, and then writes the narrative with relevant information in a conversational tone. For example, if a data analyst wants to know how a particular product is doing in a market, an advanced NLG tool would write a report by segregating the data of only the required product. Do we really need natural language generation? A number of devices are connected to the internet creating a huge Internet of Things. All these devices are creating data at a lightning speed leading to Big Data generation. It is almost humanly impossible to analyse, interpret and draw rational interference from this enormous data. Along with data analysis and accurate interpretation the need for the optimum use of resources, cost cutting and time management are the essentials for a modern business to survive, grow and flourish. Natural Language Generation helps up to effectively achieve all these goals in one go. Additionally, when a machine can do these routine tasks, and accurately. So, valuable human resources can indulge themselves in the activities that require innovation, creativity and problem-solving. Will Natural Language Generation kill jobs? First of all, not all kinds of narratives can be written by Natural Language Generation tools. It is only for creating a text based on data. Creative writing, engaging content is developed not only by analytical skills but with the help of major emotional involvement. The passion of an individual, their skills, their ability to cater complex terms in simpler formats can’t be replaced. Additionally, to rationalise the text created by Natural Language Generation tools, human intervention is critical. Natural Language Generation only augments the job and enriches the life of employees by freeing them from menial jobs. Alain Kaeser, founder of Yseop has rightly acknowledged that- “The next industrial revolution will be the artificial intelligence revolution and the automation of knowledge work and repetitive tasks to enhance human capacity”. Why should you get a hang of Natural Language Generation? A research commissioned by Forrester Research anticipated a 300% increase in investment in artificial intelligence in 2017 compared to 2016. The Artificial Intelligence market will grow from $8 billion in 2016 to more than $47 billion in 2020. Based on this report, Forbes magazine has come up with a list of the ‘hottest ten Artificial Intelligence technologies’ that will rule the market in the near future. Natural Language Generation is one of them and it is set to see a huge boost. Examples and Applications of Natural Language Generation Natural Language Generation techniques are put to use across various industries as per their requirements. Healthcare-Pharma, Banking services, Digital marketing… it’s everywhere! From fund reporting in finance and campaign analytics reporting in marketing to personalised client alerts for preparing dashboards in sales and customer service maintenance, it is used to generate effective results for all departments in an organisation. Let’s have a quick look at how NLG has varied applications in various departments: Marketing – Two main responsibilities of a marketing department are designing market strategy and conducting market research. Both of these activities heavily depend on data analysis, and in today’s world of big data, it is becoming increasingly complex. Natural Language Generation tools can help you scan big data, analyse it and write reports for you within a few hours. Sales – A sales analysis report indicates the trends in a company’s sales volume over a period of time. A sales analysis report throws light on the factors that affects sales, like season, competitors strategy, advertising efforts etc. Managers use sales analysis reports to recognise market opportunities and areas where they could increase volume. These reports are purely based on humongous data. Natural Language Generation programs save your time and efforts of manually scanning data, finding trends and writing reports. Once you feed the inputs, it takes care of all of these activities. Banking and finance – May it be a finance department of an organisation or an investment bank, financial reports stating the financial health of a company needs to be written and sent out to shareholders, investors, rating agencies, government agencies etc. The general financial statements like balance sheets, Statement of cash flows, Income statement etc. are loaded with numbers and a reader likes to have a quick understanding of these statements. Natural Language Generation software scans through these statements and presents this information in a simple, text format rather than complicated accounting one. Healthcare and medicine – Recently Natural Language Generation tools are being used to summarise e-medical records. Additional research in this area is opening doors to prudent medical decision-making for medical professionals. It is also being used in communicating with patients, as a part of patient awareness programs in India, as per the NCBI report. The data collected through medical research like what kind of lifestyle diseases are most dreadful or what kinds of habits are healthy can be summarized in a simple language for patients which is extremely useful for the doctors to make a case for their advice. And this is just the tip of the iceberg. The applications of NLG tools are widespread already and are ready to take off to greater heights in the future. Techniques of natural language generation – How to get started A refined Natural Language Generation system needs to inject some aspects of planning and amalgamation of information to enable the NLG tools to generate the text which appears natural and interesting. The general stages of natural language generation, as proposed by Dale and Reiter in their book ‘Building Natural Language Generation Systems’ are: Content determination: In this stage, a data analyst must decide what kind of information to present by using their discretion with respect to relevance. For example, deciding what kind of information a share trader would want to know vs what kind of information a dealer in the commodity market would want to know. Document structuring: In this stage, a user will have to decide the sequence, format of content and the desired template. For example, to decide the order of large cap, mid cap, small cap shares while writing a narrative about equity movement in the stock market. Aggregation: No repetition is the basic rule of any report writing. To keep it simple and improve readability, merging sentences, omitting repetitive words, phrases etc, falls under this stage. For example, if NLG software is writing a report on sales and there is no substantial change in volume of sales for a few months, there are chances NLG software might write repetitive paragraphs for no substantial information. You will then have to condense it in a way it does not become long and boring. Lingual choice: Deciding what words to use exactly to describe particular concepts. For example, deciding whether to use the word ‘medium’ or ‘moderate’ while describing a change. Best software products available for natural language generation There are a variety of software products available to help you get started with Natural Language Generation. Quill, Syntheses, Arria, Amazon Polly, Yseop are popular ones. You can make a decision based on the industry you are operating in, for the department you will be deploying the tool, exact nature of report creation, etc. Let us see what kind of aid does these programs offer to the businesses. Yseop: Yseop Compose’s Natural Language Generation software enables data-driven decision making by explaining insights in a plain language. Yseop Compose is the only multilingual Natural Language Generation software and hence truly global. Amazon Polly: It is a software that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products. Arria: Arria NLG Platform is the one that integrates cutting-edge techniques in data analytics, artificial intelligence and computational linguistics. It analyses large and diverse data sets and automatically writes tailored, actionable reports on what’s happening within that data, with no human intervention, at vast scale and speed. Quill: It is an advanced NLG platform which comprehends user intent and performs relevant data analysis to deliver Intelligent Narratives—automated stories full of audience-relevant, insightful information. Synthesys: It is one of the popular NLG software products that scans through all data and highlights the important people, places, organizations, events and facts being discussed, resolve highlighted points and determines what’s important, connecting the dots together and figures out what the final picture means by comparing it with the opportunities, risks and anomalies users are looking for. Natural Language Generation tools automate analysis and increase the efficacy of Business Intelligence tools. Rather than generating charts and tables, NLG tools interpret the data and draft analysis in a written form that communicates precisely what’s important to know. These tools perform regular analysis of predefined data sets, eliminate the manual efforts required to draft reports and the skilled labour required to analyse and interpret the results. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau What are the best resources to learn Natural Language Generation? Gartner, a leading research and advisory company forecasts that most companies will have to employ a Chief Data officer by 2019. With the gigantic amount of data available, it is important to decide which information can add business value, drive efficiency and improve risk management. This will be the responsibility of Data Officers. With increasing global demand for the profession, there can be no better time to learn about Natural Language Generation which is a critical part of Data Science and Artificial Intelligence. Though Natural Language generation has a huge scope, there are very few comprehensive academic programs designed to train candidates to be future ready. However, with a great vision, UpGrad offers a PG Diploma in Machine Learning and AI, in partnership with IIIT-Bangalore, which aims to build highly skilled professionals in India to cater to the increasing global demand. It gives you a chance to learn from a comprehensive collection of case-studies, hand-picked by industry experts, to give you an in-depth understanding of how Machine Learning & Artificial Intelligence impact industries like Telecom, Automobile, Finance & more. What are you waiting for? Don’t let go of this wonderful opportunity, start exploring today!

by Maithili Pradhan

30 Jan'18

8.3K+

A Beginner’s Guide To Natural Language Understanding

“A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.” – Alan Turing Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses The entire gamut of artificial intelligence is based on machines being able to ‘understand’ and ‘respond’ to human beings. Which is impossible without the capability of machines to interact with humans in their natural language, like other human beings. Moreover, understanding does not involve the mere exchange of information and data but an exchange of emotions, feelings, ideas and intent. Can machines ever do that? Well, the answer is affirmative and it is not even that surprising anymore. What is this miraculous technology that smoothly facilitates the interaction between humans and machines? It is Natural Language Understanding. What is Natural Language Understanding? Natural Language Understanding is a part of Natural Language Processing. It undertakes the analysis of content, text-based metadata and generates summarized content in natural, human language. It is opposite to the process of Natural Language Generation. NLG deals with input in the form of data and generates output in the form of plain text while Natural Language Understanding tools process text or voice that is in natural language and generates appropriate responses by summarizing, editing or creating vocal responses. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Get Machine Learning Certification from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Natural Language Understanding Vs Natural Language Processing Natural Language Processing is a wide term which includes both Natural Language Understanding and Natural Language Generations along with many other techniques revolving around translating and analysing natural language by machines to perform certain commands. Examples of Natural Language Processing Natural Language Processing is everywhere and we use it in our daily lives without even realising it. Do you know how spam messages are separated from your emails? Or autocorrect and predictive typing that saves so much of our time, how does that happen? Well, it is all part of Natural Language Processing. Here are some examples of Natural Language Processing technologies used widely: Intelligent personal assistants – We are all familiar with Siri and Cortana. These mobile software products that perform tasks, offer services, with a combination of user input, location awareness, and the ability to access information from a variety of online sources are undoubtedly one of the biggest achievements of natural language processing. Machine translation – To read a description of a beautiful picture on Instagram or to read updates on Facebook, we all have used that ‘see translation’ command at least once. And google translation services helps in urgent situations or sometimes just to learn few new words. These are all examples of machine translations, where machines provide us with translations from one natural language to another. Speech recognition – Converting spoken words into data is an example of natural language processing. It is used for multiple purposes like dictating to Microsoft Word, voice biometrics, voice user interface, etc. Affective computing – It is nothing but emotional intelligence training for machines. They learn to understand your emotions, feelings, ideas to interact with you in more humane ways. Natural language generation – Natural language generation tools scan structured data, undertake analysis and generate information in text format produced in natural language. Natural language understanding – As explained above, it scans content written in natural languages and generates small, comprehensible summaries of text. Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Best tools for Natural Language Understanding available today Natural Language Processing deals with human language in its most natural form and on a real-time basis, as it appears in social media content, emails, web pages, tweets, product descriptions, newspaper articles, and scientific research papers, etc, in a variety of languages. Businesses need to keep a tab on all this content, constantly. Here are a few popular natural language understanding software products which effectively aid them in this daunting task. Wolfram – Wolfram Alpha is an answer engine developed by Wolfram Alpha LLC (a subsidiary of Wolfram Research). It is an online service that provides answers to factual questions by computing the answer from externally sourced, “curated data”. Natural language toolkit – The Natural Language Toolkit, also known as NLTK, is a suite of programs used for symbolic and statistical natural language processing (NLP) for the English language. It is written in the Python programming language and was developed by Steven Bird and Edward Loper at the University of Pennsylvania. Stanford coreNLP – Stanford CoreNLP is an annotation-based NLP pipeline that offers core natural language analysis. The basic distribution provides model files for the analysis of English, but the engine is compatible with models for other languages. GATE (General Architecture for Text Engineering) – It offers a wide range of natural language processing tasks. It is a mature software used across industries for more than 15 years. Apache openNLP – The Apache OpenNLP is a toolkit based on machine learning to process natural language text. It is written in Java and is produced by Apache software foundation. It offers services like tokenizers, chucking, parsing, part of speech tagging, sentence segmentation, etc. Applications of Natural Language Understanding As we have already seen, natural language understanding is basically nothing but a smart machine reading comprehension. Now let’s have a close look at how it is used to promote the efficiency and accuracy, while saving time and efforts, of human resources, which can then be put to better use. Collecting data and data analysis – To be able to serve well, a business must know what is expected out of them. Data on customer feedback is not numeric data like sales or financial statements. It is open-ended and text heavy. For companies to identify patterns and trends throughout, this data and taking action as per identified gaps or insights, is crucial for survival and growth. More and more companies are realizing that implementing a natural language understanding solution provides strong benefits to analysing metadata like customer feedback and product reviews. Natural language understanding in such cases proves to be more effective and accurate than traditional methods like hand-coding. It helps the customer’s voice to reach you clearer and faster, which leads to effective strategizing and productive implementation. Reputation monitoring – Customer feedback is just a tip of the iceberg as compared to the real feelings of customers about the brand. As customers, we hardly participate in customer survey feedbacks. Most of the real customer sentiments hence are trapped in unstructured data. News, blog posts, chats, and social media updates contain huge amounts of such data which is more natural and can be used to know the ‘real’ feelings of customers about the product or service. Natural language understanding software products help businesses to scan through such scattered data and draw practical inferences. Customer service – Natural Language Understanding is able to communicate with untrained individuals and can understand their intent. NLU is capable of understanding the meaning in spite of some human errors like mispronunciations or transposed letters or words. It also uses algorithms that break down human speech to structured ontology and fishes out the meaning, intent, sentiment, and the crux of human speech. One of the most important goals of NLU is to create chatbots or human interacting bots that can effectively communicate with humans without any human supervision. There are various software products like Nuance which are already involved in customer interaction. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Automated trading – Capital market trading automation is not a new phenomenon anymore. Multiple software products and platforms are now available that analyse market movements, the profile of industries and financial strength of a company and based on technical analysis design the trading patterns. Advanced Natural Language Understanding tools which scan through various sources like financial statements, reports, market news are the basis of automated trading systems. Market Intelligence – “What are competitors doing?” is one of the most critical information businesses need on a real-time basis. Information influences markets. Information exchange between various stakeholders designs and redesigns market dynamics all the time. Keeping a close watch on the status of an industry is essential to developing a powerful strategy, but the channels of content distribution today (RSS feeds, social media, emails) generate so much information that it’s been increasingly difficult to keep a tab on such unstructured, multi-sourced content. Financial markets have started using natural language understanding tools rigorously to keep track of information exchange in the market and help them reach it immediately. Due to such varied functions carried out by natural language understanding programs, its importance in trade, business, commerce and the industry is ever increasing. It is a smart move to learn natural language understanding programs to ensure yourself a successful career. What is the best way to learn Natural Language Understanding? The best way to prepare yourself for a brighter future in technological endeavors is to understand the algorithms of Artificial intelligence. The Post Graduate Diploma in Machine Learning and AI by UpGrad offers a chance to master concepts like Neural Networks, Natural Language Processing, Graphical Models and Reinforcement Learning. The most unique aspect of this course is the career support. And, the industry mentorship, which will help you prepare yourself for intense competition in the industry, within your actual job. So, let’s learn to use software products widely used in industry mentioned earlier like NLKT. This program aims at producing well-rounded data scientists and AI professionals with thorough knowledge of mathematics, expertise in relevant tools/languages and understanding of cutting-edge algorithms and applications. Start preparing today for a better tomorrow! Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

by Maithili Pradhan

30 Jan'18

10.99K+

Neural Networks for Dummies: A Comprehensive Guide

Our brain is an incredible pattern-recognizing machine. It processes ‘inputs’ from the outside world, categorizes them (that’s a dog; that’s a slice of pizza; ooh, that’s a bus coming towards me!), and then generates an ‘output’ (petting the dog; the yummy taste of that pizza; getting out of the way of the bus!). Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses All of this with little conscious effort, almost impulsively. It’s the very same system that senses if someone is mad at us, or involuntarily notices the stop signal as we speed past it. Psychologists call this mode of thinking ‘System 1’, and it includes innate skills — like perception and fear — that we share with other animals. (There’s also a ‘System 2’, to know more about it, check out the extremely informative Thinking, Fast and Slow by Daniel Kahneman). How is all of this related to Neural Networks, you ask? Wait, we’ll get there in a second. Look at the image above, just your regular numbers, distorted to help you explain the learning of Neural Networks better. Even looking cursorily, your mind will prompt you with the words “192”. You surely didn’t go “Ah, that seems like a straight line, I think it’s a 1”. You didn’t compute it – it happened instantly. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Fascinating, right? There is a very simple reason for this – you’ve come across the digit so many times in your life, that by trial and error, your brain automatically recognizes the digit if you present it with something even remotely close to it. Let’s cut to the chase. What exactly is a Neural Network? How does it work? By definition, a neural network is a system of hardware or softwares, patterned after the working of neurons in the human brain. Basically, it helps computers think and learn like humans. An example will make this clearer: As a child, if we ever touched a hot coffee mug and it burnt us, we made sure not to touch a hot mug ever again. But did we have any such concept of hurt in our conscience BEFORE we touched it? Not really. This adjustment of our knowledge and understanding of the world around us is based on recognizing patterns. And, like us, computers, too, learn through the same type of pattern recognition. This learning forms the whole basis of the working of neural networks. Traditional computer programs work on logic trees – If A happens, then B happens. All the potential outcomes for each of the systems can be preprogrammed. However, this eliminates the scope of flexibility. There’s no learning there. And that’s where Neural Networks come into the picture! A neural network is built without any specific logic. Essentially, it is a system that is trained to look for and adapt to, patterns within data. It is modeled exactly after how our own brain works. Each neuron (idea) is connected via synapses. Each synapse has a value that represents the probability or likelihood of the connection between two neurons to occur. Take a look at the image below: What exactly are neurons, you ask? Simply put, a neuron is just a singular concept. A mug, the colour white, tea -, the burning sensation of touching a hot mug, basically anything. All of these are possible neurons. All of them can be connected, and the strength of their connection is decided by the value of their synapse. Higher the value, better the connection. Let’s see one basic neural network connection to make you understand better: Each neuron is the node and the lines connecting them are synapses. Synapse value represents the likelihood that one neuron will be found alongside the other. So, it’s pretty clear that the diagram shown in the above image is describing a mug containing coffee, which is white in colour and is extremely hot. All mugs do not have the properties like the one in question. We can connect many other neurons to the mug. Tea, for example, is likely more common than coffee. The likelihood of two neurons being connected is determined by the strength of the synapse connecting them. Greater the number of hot mugs, the stronger the synapse. However, in a world where mugs are not used to hold hot beverages, the number of hot mugs would decrease drastically. Incidentally, this decrease would also result in lowering the strength of the synapses connecting mugs to heat. So, Becomes This small and seemingly unimportant description of a mug represents the core construction of neural networks. We touch a mug kept on a table — we find that it’s hot. It makes us think all mugs are hot. Then, we touch another mug – this time, the one kept on the shelf – it’s not hot at all. We conclude that mugs in the shelf aren’t hot. As we grow, we evolve. Our brain has been taking in data all this time. This data makes it determine an accurate probability as to whether or not the mug we’re about to touch will be hot. Neural Networks learn in the exact same way. Now, let’s talk a bit aboutthe first and the most basic model of a neural network: The Perceptron! What is a Perceptron? A perceptron is the most basic model of a neural network. It takes multiple binary inputs: x1, x2, …, and produces a single binary output. Let’s understand the above neural network better with the help of an analogy. Say you walk to work. Your decision of going to work is based on two factors majorly: the weather, and whether it is a weekday or not. The weather factor is still manageable, but working on weekends is a big no! Since we have to work with binary inputs, let’s propose the conditions as yes or no questions. Is the weather fine? 1 for yes, 0 for no. Is it a weekday? 1 yes, 0 no. Remember, we cannot explicitly tell the neural network these conditions; it’ll have to learn them for itself. How will it decide the priority of these factors while making a decision? By using something known as “weights”. Weights are just a numerical representation of the preferences. A higher weight will make the neural network consider that input at a higher priority than the others. This is represented by the w1, w2…in the flowchart above. “Okay, this is all pretty fascinating, but where do Neural Networks find work in a practical scenario?” Real-life applications of Neural Networks If you haven’t yet figured it out, then here it is, a neural network can do pretty much everything as long as you’re able to get enough data and an efficient machine to get the right parameters. Anything that even remotely requires machine learning turns to neural networks for help. Deep learning is another domain that makes extensive use of neural networks. It is one of the many machine learning algorithms that enables a computer to perform a plethora of tasks such as classification, clustering, or prediction. With the help of neural networks, we can find the solution of such problems for which a traditional-algorithmic method is expensive or does not exist. Neural networks can learn by example, hence, we do not need to program it to a large extent. Neural networks are accurate and significantly faster than conventional speeds. Because of the reasons mentioned above and more, Deep Learning, by making use of Neural Networks, finds extensive use in the following areas: Speech recognition: Take the example of Amazon Echo Dot – magic speakers that allow you to order food, get news and weather updates, or simply buy something online just by talking it out. Handwriting recognition: Neural networks can be trained to understand the patterns in somebody’s handwriting. Have a look at Google’s Handwriting Input application – which makes use of handwriting recognition to seamlessly convert your scribbles into meaningful texts. Face recognition: From improving the security on your phone (Face ID) to the super-cool Snapchat filters – face recognition is everywhere. If you’ve ever uploaded a photo on Facebook and were asked to tag the people in your photo, you know what face recognition is! Providing artificial intelligence in games: If you’ve ever played chess against a computer, you already know how artificial intelligence powers games and game development. It’s to the extent that players use AI to improve upon their tactics and try their strategies first-hand. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau In Conclusion… Neural networks form the backbone of almost every big technology or invention you see today. It’s only fair to say that imagining deep/machine learning without neural networks is next to impossible. Depending on the way you implement a network and the kind of learning you put to use, you can achieve a lot out of a neural network, as compared to a traditional computer system. Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

by Reetesh Chandra

06 Feb'18