The two paths from Natural Language Processing to Deep Learning

Posted on June 30, 2022August 29, 2022 by Imarticus Learning

The two paths from Natural Language Processing to Deep Learning

Natural Language Processing is a branch of linguistics, computer science, and artificial intelligence that deals with the interaction between computers and human language, in particular how to design computers to handle and evaluate huge volumes of natural language data. We want a computer that can “understand” the text in documents, including its context and subtleties.

As a result, the papers’ data and insights may be correctly extracted by the technology, which can also classify and arrange the documents themselves.

Massive amounts of unprocessed, text-heavy data need a system like this, which is widely used in machine learning. Professionals with expertise in designing models that analyze voice and language, find contextual correlations, and generate insights from this unprocessed data will be in high demand as AI continues to grow. Natural Language Processing and Deep Learning with Python are one of the most common phrases used in the domain of Artificial Intelligence nowadays.

In machine learning and artificial intelligence, a technique known as “deep learning” mimics human learning processes. Data science, which encompasses the statistical analysis and forecasting models, relies heavily on deep learning techniques to do its work. For data scientists, deep learning is a godsend since it speeds up the process of processing and understanding massive volumes of data.

It is possible to think of deep learning as the automation of predictive analytics. Deep learning algorithms are piled in a hierarchical structure of increasing complexity and abstraction, while typical machine learning algorithms are linear.

Neural Networks and Deep Learning

A Neural Network, also known as an Artificial Neural Network, is made up of layers. Imagine the neurons in a human brain; they are the computing units, and they form a single layer when layered. stacking neurons together creates several layers. It is termed the input layer because it contains the data that we are working with. We run our algorithms and get an output, which is then utilized to do our computations for the following layer, the output layer.

At each successive layer, all of one layer’s neurons are linked to those at each successive layer, which are then linked to the next layer, and so on, until we reach our output layer, where we achieve our desired outcome for the specific data we were working with. Those layers that are between the input and output layers are referred to as Hidden Layers. A Neural Network is the result of this process.

A deep neural network is an artificial neural network with two or more hidden layers, and a model built on a deep neural network is referred to as Deep Learning.

What are the main components of Natural Language Processing?

NLP consists of a number of components, a few of them are mentioned below:

Analysis of morphological and lexical patterns.
Syntactic Analysis: Study of logical meaning from a given part of the information, be it text or audio.
Semantic Analysis: Used to analyze the meaning of words.

How Can Computer Vision Protect Millions of Homes From Intrusion?

Posted on September 8, 2021March 22, 2024 by Imarticus Learning

Introduction

We need to embrace the concept of computer vision in homes rather than shy away from the idea of exchanging personal data to achieve new levels of protection, safety, comfort, and entertainment. Computer vision combined with NLP and ML enables computers/systems via digital images or video to understand what they see.

When systems can detect and recognize objects, according to what they are scheduled to do, they can deliver intelligent behavior. Automotive space is one area that has successfully demonstrated how computer vision can change our lives. Car systems that use computer vision can recognize the driver behind the wheel and can warn the driver when he starts to swerve out of his lane to see the surrounding area.

Many customers on their smartphones are already using computer vision and don’t even know it. To recognize facial features and position overlays (philters) in the right positions, both Snapchat and Instagram use computer vision tracking.

How does Computer Vision help us in making things secure?

Accepting computer vision into your house and connecting it to your connected devices helps your daily routine to have a new level of convenience. When you arrive and open the door for someone, the front door will be able to see or stay locked when an unknown person (face) approaches. Alarm systems are smarter, able to distinguish who are family members (including age and gender) and who are not.

If an elderly family member or visitor trips, or if a child is climbing up the stairs, on the countertop, or anywhere that puts the child in danger, indoor surveillance cameras will send a warning to your mobile, taking it a step further. Nest, Logitech, and other smart home manufacturers have either begun offering customers these smart security features as a premium subscription service or have already incorporated them into their newest devices.

Computer Vision in Intrusion Detection

Abbreviated as IDS, an Intrusion Detection system plays an important role in providing the required security assurances for all networks and information systems in the world. One of the solutions used to decrease malicious attacks is IDS. As attackers often change their attack tactics and find new methods of attack, IDS must also develop by implementing more sophisticated detection methods in response.

The enormous data growth and substantial developments in computer hardware technology have led to the existence of new studies in the field of deep learning, including intrusion detection.

To provide a high degree of security and security staff monitoring effectiveness, high-performance AI systems can make the task monitoring process automatic for high-risk sites. Also, these intrusion systems can identify objects based on size and location. However, they fail to recognize the type or form of the detected object.

Perimeter Defense (Intrusion Detection) systems with high-end artificial AI algorithms to identify a multitude of different types of objects can now discern objects of interest, thus dramatically reducing the rate of such intrusions that might indicate a false alarm. The more sophisticated systems, such as those provided at IronYun, allow its customers to design ROIs based on intrusion detected points, high-value areas, and or any other region that may be beneficial for alerts.

Similarly, the applications designed for face and license plate recognition have the ability to detect people or cars(the license plate) in addition to solutions for motion detection and use pre-designed data to identify distinct faces or plates that should be watched regularly, similar to the pre-designed lists.

Needless to say that these systems will also allow its customers to search for faces that are not provided already on the camera. For example, if a person is identified hanging outside a house many times, one can store their pictures in the designed watchlist and fix an alarm when the face is identified again around the house or in your surroundings.

The main advantage of the system is that before the troublemaker completes the act, the warnings will assist in discouraging and avoiding vandalism or robbery and inform the authorities of the scene.

Conclusion

AI-based security measures combined with computer vision, deep learning, ML, and NLP training can do all the boring work for you to help deter fraud and vandalism. They are also the most accessible security solutions available with a strong return on investment due to their low cost and outstanding reliability.

Stopping crime is a challenging, ongoing challenge, but enterprise vendors and law enforcement can do it more easily with the right AI apps. This is also one of the reasons why people are excited about an acceptable career in the AI sector.

Solve Real-world Text Analytics Problems With NLP!

Posted on April 27, 2021April 11, 2024 by Imarticus Learning

Solve Real-world Text Analytics Problems With NLP!

Natural language processing (NLP) helps machines analyze text or other forms of input such as speech by emulating how the human brain processes languages like English, French, or Japanese. NLP consists of ‘natural language understanding’ and ‘natural language generation’ which help machines create a summary of the information or assist in taking part in conversations.

With the advent of natural language processing, services like Cortana, Siri, Alexa, and Google Assistant are finding it easier to analyze and respond to requests from users. This is opening up many new possibilities in human-machine interactions and helping improve existing systems and services.

In this article, we will cover how NLP is helping provide solutions for various requirements of text analytics in different sectors.

Significance of NLP in modern times

NLP can analyze massive amounts of text-based data with consistency and accuracy. NLP courses help summarize key concepts from large unstructured complex texts. It also helps in deciphering or analyzing ambiguous statements or sentences. It can draw connections and also investigate deeper meanings behind seemingly normal data in the form of text.

With the massive amounts of randomized forms of textual data that is generated on a daily basis, automation is highly necessary for this field to analyze the large amounts of data from text efficiently and effectively. Ranging from text posted on social media to customer service, natural language processing is powering text analytics which is making life easier for both consumers and corporations.

How text analytics along with NLP is helping businesses?

Text analytics can be described as a process of analyzing a massive or specifically targeted volume of unstructured textual data and translating it into quantitative information to gain valuable insights through patterns and trends.

With the help of additional visualization of this data, text analytics allows corporations to understand the sentiments, deeper meaning, or compact information behind this data and helps them take data-backed or data-centric decisions for improved results through better performance or profit.

These companies collect massive amounts of unstructured textual data from sources like social media, e-mails platforms, chat services, and historic data from previous interactions or third parties. This could prove to be a challenge without the help of natural language processing which powers text analytics, helping analyze the massive amounts of data without the need to stop or for human interference.

The same amount of data, being manually processed seems like an impossible, never-ending task. Manually processing even a tiny bit of the colossal amount of data that is generated daily would definitely take a lot of manpower. Hence, it is not cost-effective and would also lead to inaccuracy and duplication. This is where text analytics comes to the rescue.

With the help of text analytics, companies can excavate meaning and sentiments from unstructured textual data sourced from social media posts, content inside e-mails, chat services, and surveys or feedback.

This helps businesses identify patterns and trends which lead to providing customers with improved experiences by analyzing service or product issues and customer expectations through market research and monitoring with text analytics.

Here are some real-world applications of text analytics and natural language processing:

Customer care service

Data generated from surveys, chats, and service tickets can help companies improve the quality of customer service by increasing efficiency and decreasing the time taken in resolving problems.

Illegal activity and fraud detection

Text analytics helps in analyzing unstructured data from various internal or external sources to prevent fraud and warn governments or companies of illegal and fraudulent activities.

Social media analytics

Text analytics is being used by brands to analyze customer preferences and expectations through the extraction of sentiments and summarized opinions from textual data sourced from social media platforms like Facebook and Instagram.

Text analytics and NLP are increasingly becoming more effective for companies to depend on and encouraging them to take more data-backed decisions. This need is making way for better, more accurate, and faster analytical tools and technologies in the future.

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