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Tag: deep learning

An Introduction to Deep Learning and Neural Networks: How They Transform Business Analytics

Posted on by Imarticus Learning

Imagine a world where there is absolute precision in predicting the behavior of customers, prevention of fraud before it even occurs and experiences become so seamlessly personalized that every interaction feels like a perfect fit. This isn’t just a dream of the future – Deep Learning and Neural networks are building this into our current reality with business analytics. By mimicking the very patterns of how our brains can learn and adapt, the cutting-edge technology is more than just running numbers – it’s uncovering patterns, smarter decision-making and changing entire industries.

With the advent of AI, there has been massive change in the field of Business Analytics. One of the most striking developments among others is that of Deep Learning. But what exactly is Deep Learning and how does it integrate into business analytics? This blog answers all your questions about its applications, benefits, and potential in shaping new industries.

What is Deep Learning?

Deep Learning is the subdivision of machine learning that works using artificial neural networks to represent a human brain’s working pattern. These neural networks have several layers in which interconnected nodes, or rather neurons, process and analyze large amounts of data. Unlike other traditional algorithms for machine learning, Deep Learning models excel in recognizing patterns, making predictions, and solving complex problems even without being explicitly programmed to perform certain tasks.

Deep Learning offers a power that is unmatched within business analytics, allowing the realization of deeper insights from data and decision-making processes.

Role of Neural Networks in Deep Learning

At the core of Deep Learning there exists a model called neural networks which is inspired from the structure and function of the human brain and is also regarded as a computational model. These models are designed to learn data, identify patterns, and adapt over time, thereby making them very effective in a broad variety of applications.

Key Components of Neural Networks:

Input Layer: The input layer receives the raw data.

Hidden Layers: The hidden layers perform computations to detect intricate patterns.

Output Layer: It is the output of the final result or prediction.

With several hidden layers, neural networks applications in business have grown to be used in solving challenges like customer segmentation, fraud detection, and predictive maintenance.

How Deep Learning Changes Business Analytics

Deep Learning has opened new avenues for businesses looking to compete in the world of data. This is how it changes the landscape:

Improved Data Processing

The traditional analytics techniques do not support unstructured data like images, videos, or text. The Deep Learning models can work through these types of data and allow access to previously unknown insights for businesses.

Better Decision-Making

It has been possible to forecast happenings of the future on the basis of past information trends through the application of Deep Learning models. Improvements have been allowed in demand forecasting in retail and stock price forecasting in finance, besides other areas, because of such capability.

Personalized Customer Experience

Deep Learning empowers hyper-personalization based on the analysis of customers’ behavior and choices. The best examples that depict Deep Learning in Business Analytics are AI-based recommendation engines from Netflix and Amazon.

Real-Time Analytics

Through real-time data processing, Deep Learning models allow businesses to react to changes in the market in a timely manner. This is particularly important in e-commerce and logistics.

Cost Optimization

AI in business effectively reduces operational costs while adapting them to increased productivity in the organizations. This is achieved through automating those endless activities, thus improving the efficiencies of different processes.

Real-World Applications of Neural Networks in Business

The versatility of neural networks makes them indispensable in modern business operations. Let’s explore some prominent neural networks applications in business:

Fraud Detection

Neural networks are used in finance to identify fraudulent transactions by analyzing large data sets for patterns and anomalies. They are constantly learning from the historical data and thus improving the capabilities of fraud detection.

Predictive Maintenance

In manufacturing and aviation, neural networks analyze sensor data to predict equipment failures. This proactive approach minimizes downtime and reduces maintenance costs.

Customer Segmentation

Businesses segment customers using neural networks by their behavior, demographics, or purchasing history. This makes for focused marketing and better customer retention.

Chatbots and Virtual Assistants

Deep Learning has powered AI chatbots to change customer service by giving real-time answers to questions. The same tool also improves the user experience as it learns from past experiences.

Sentiment Analysis

Brands use neural networks to scan for customer sentiment on social media, reviews, and surveys. This allows them to know how people feel about their products or services and modify their marketing approaches accordingly.

Deep Learning Benefits in Business Analytics

Implementing Deep Learning in business analytics is no longer an option but a necessity for any business looking to get ahead in competition. Here are some of the main benefits:

Scalability

Deep Learning models can process huge amounts of data, thus making them fit for companies that handle big data.

Precision

From complex datasets, Deep Learning can make very accurate predictions and even insights that decrease the error margin of a decision made.

Automation

Deep Learning automates processes that were previously required to be performed by man, such as data categorisation, image recognition, and natural language processing, among many others.

Personalization

With the ability to understand customer behavior, businesses can deliver experiences that drive loyalty and satisfaction.

Innovation

Deep Learning enables innovation in new products and services, such as autonomous vehicles, smart home devices, and advanced healthcare solutions.

Challenges in Implementing Deep Learning

There are challenges associated with adopting Deep Learning:

Data Requirements: Deep Learning models require huge amounts of quality data, which is often difficult to obtain.

Computational Power: Training Deep Learning models requires a lot of computational resources, including GPUs and TPUs.

Interpretability: Neural networks often work like “black boxes,” and it is difficult to understand how decisions are made.

Cost: Implementing Deep Learning solutions can be expensive, especially for small and medium-sized enterprises.

A strategic approach has to be taken, from the right infrastructure investment, developing the right talent, and an appropriate phased implementation.

Deep Learning and Business Future

Digital transformation continues to take hold within business enterprises. The role that Deep Learning will play in that success will only expand in the future. The trends for businesses in this area include:

Integrate with IoT: Deep Learning is going to enhance IoT because it will make smarter devices and real-time analytics possible.

Ethical AI: There will be a focus on developing ethical AI frameworks as data privacy and bias concerns grow.

Democratisation of AI: Tools and platforms that make Deep Learning easy to implement will make the technology accessible to smaller businesses.

Industry-Specific Applications: Sector-specific Deep Learning solutions will emerge for healthcare, education, and agriculture.

Conclusion

Undeniably, Deep Learning and neural networks hold transformative powers in their application to businesses. This ranges from improvements in processing data to the realization of more personalized customer experiences, altering the nature of how organizations consider analytics and decision-making. And for those Indian professionals wanting to join the analytics and AI bandwagon, this is the ideal time to upskill in it. While the challenges of implementation cannot be ignored, the benefits are far more worth it. The more organisations dive into understanding how Deep Learning changes business, the more their ability to innovate and generate efficiency in their respective fields will grow.

In a data-driven world, the adoption of AI in business is not just a recommendation but a compulsion. Being a professional seeking a boost for his or her career prospects or a business leader attempting to remain at the sharp end of the curve, knowing and exploiting deep learning capabilities opens doors toward unprecedented growth and success opportunities. By integrating Deep Learning in business analytics, companies can pave the way for smarter, faster, and more efficient operations. The possibilities are limitless as we continue to harness the potential of AI. Are you ready to be part of this transformation?

Does Machine Learning Excite You? Check Out Our Data Analytics Course!

Posted on by Imarticus Learning

Machine learning (ML) is truly a blessing to modern computing and technology, possessing the ability to endow systems and machines, the ability to think for themselves and tackle tasks on their own without any supervision of humans. Machine learning is able to do this by creating artificial neural networks which simulate how human brains work. Machine learning is assisted by data science and supports its applications in various fields.

Even though machine learning was initially invested upon with the primary focus on Artificial Intelligence, it was later recognized as a separate field and started being heavily invested upon from the 1990s and is one of the most valuable fields of computing that has one of the highest industry requirements of skilled professionals and freshers holding expertise in various skills and tools which assist in machine learning.

In this article, we will learn more about machine learning and how a well-planned data analytics course can help you progress in your career if you are already in this field or how it can help freshers get exposed to ML. 

What is machine learning?

Machine learning first came into existence due to the interest of having systems and computers learn from data on their own. “Machine learning” was first termed by Arthur Samuel in 1959, who was working in IBM at that time. During his tenure there, he was responsible for various important projects related to computer gaming and AI. It all started when Mr. Samuel took the initiative to teach computers how to play games through the game of Checkers on IBM’s first commercially available computer, the IBM 701.

Eventually, machine learning started being used for various purposes and borrowed many models and approaches from statistics and probability theory. AI uses predictive analytics along with machine learning to execute the various responses or trigger actions. All of this is acquired from the training data set which helps the machine in learning and equips it with the information.

Machine learning is an important branch of computing and data science that creates autonomous systems which learn from data on their own. A machine trained with clean processed data eventually identifies trends and patterns to respond to situations without human supervision.

Machine learning also promotes the automatic improvement and development of algorithms or data models which improve on their own. Machine learning is an important part of Artificial Intelligence which uses data mining, predictive analytics, and various tools to assist machines in learning more extensively with methods like deep learning to allow them to execute functions that emulate the responses of a human, just much more accurate and fast.

Machine learning is also not biased unless specifically asked to do so, hence promoting unbiased AI-supported systems that make fewer errors. Data mining is also a very relevant field and quite valuable to machine learning as it helps systems come to conclusions without having some bits of data or having unknown bits of information. Machine learning is a type of predictive analytics which is backed by data and is exploratory in nature.

Perks of a Data Science Prodegree from Imarticus

The Data Science Prodegree is a great data science course that students and working professionals can choose to gain more exposure and skills in the fields of machine learning, business analytics, and AI.

 

  • Acquire skills and learn how to use required tools and algorithms
  • Gain valuable industry and course certifications
  • Get placement support and opportunities from the best companies
  • Advanced live classroom learning supported by technology and real-life projects

 

Imarticus’s Data Science course with Placement is a great choice if you wish to advance in your career and learn about machine learning, AI, business analytics, or data analysis which will help you become more effective as a data scientist and pursue your dream career in this respectable field.

A Complete Guide On How To Approach A Machine Learning Problem For Beginners!

Posted on by Imarticus Learning

As beginners in machine learning, you will want to have questions answered to common problems. Questions like how to approach, how to start, which algorithm fits best, and so on.

Common problems in machine learning for beginners

Here, we will help you resolve those problems by answering common questions:

Where can you use machine learning?

You can use machine learning for problems when:

  • Automation is involved
  • Learning from data is needed
  • An estimated outcome is required
  • Need to understand pattern like user sentiments and developing recommendation systems
  • Object required to identify or detect an entity

How to solve machine learning problems?

Here are steps to solve problems in machine learning:

  • Read data from JSON and CSV
  • Identify dependent and independent variables
  • Find out if there are missing values in the data or if it is categorical
  • Apply pre-processing data methods if there are missing data to bring it in a go to go format
  • Split data in groups for testing and training for concerned purposes
  • Spilt data and fit into a suitable model and move on validating the model
  • Change parameters in the model if needed and keep up the testing
  • An optional step is to switch algorithms to get different answers to the same problem and weigh the accuracies for a better understanding – this explains the accuracy paradox
  • Visualize the results to understand where the data is headed and to explain better while representing it

What algorithm should you use?

You need to understand what labelling is to answer this. Labels are the values we need to make an estimate. This represents the Y variable, also known as the dependent variable.

Here is a small example to help you understand this:

if

dependent_variable_exists==True:

supervised learning()

else:

unsupervised learning()

Machine Learning CourseWhile you’re learning from a machine learning course, you will understand that your supervision and training refers to supervised learning. This means that the results need to be compared by a frame. The frame here is the dependent variable. However, there is no reference for frame under unsupervised learning, which is why the name.

It is time to figure out how algorithms are served. However, it is essential to note that this is a generalized approach. The situations can differ, and so will be the usage of algorithms:

  • Numeric data for linear regression
  • Logistic regression when the variable is binary
  • Multiple category classification through a linear discriminant approach
  • Decision Tree, Naive Bayes, KNN, and Ensembles for regression and classification

Machine Learning Course

As you grow in your machine learning career, you will learn how to take random XG boost, forest, Adaboost, among other algorithms for ensembles. You can try these for both regression and classification.

Ensembles, as the name goes, refer to a group of at least two classifiers or regressors. Moreover, it doesn’t matter if it is the same or if working towards the same goals.

Building visualizations

Here are some of the things to remember when visualizing reports:

  • You can show class clustering with a scatter plot
  • Avoid scatter plot if there are several data points
  • Class comparisons can be explained through histogram
  • Creating pie charts help comparative breakdown
  • Line charts can help analyze reports with frequent deviations like stocks

If a scatter plot has too many data points, it will look clumsy. It will not be a presentable representation to show stakeholders. In such cases, you should use scatter charts.

Final thoughts

These points will help a beginner in machine learning career to become more aware of how to solve problems. You now know the essential things to do and things to avoid to get accurate results.

What Are the Algorithms in Machine Learning? How Does It Work?

Posted on by Imarticus Learning

Machine learning is a vast field comprising of various data related operations such as analysis, prediction, decision making and much more. These applications require a set of well-defined steps to proceed with the idea designed for model construction. A set of well-defined instructions that produces some output or accomplishes a particular task is called an algorithm. The machine learning algorithms are broadly classified into 3 categories – Supervised, Unsupervised and Reinforcement Learning.

To choose an appropriate algorithm in machine learning, identifying the kind of problem is very necessary as each of these algorithms obeys a different plan of attack to deal with the proposed problem. Supervised learning uses an approach where the output is already known to the user or the individual while unsupervised learning concentrates on the concept of similarity in properties of the objects. Reinforcement learning differs from both of them and uses the art of learning from experiences.

Supervised learning

Supervised learning is used in machine learning tasks such as classification, regression, and analysis. It is considered as a concept that deals with labeled values. This means that the objects are categorized or assigned to different classes based on their properties. The algorithm implementation in supervised learning is done by a two-step procedure namely model construction and model utilization.

Firstly, the given data is cleaned and divided into training and testing sets. The model gains the ability to produce output by learning from the instances contained in the training set. The test set gives a measure of the model performance by producing accuracy. The accuracy indicates the amount or rather the percentage of unseen data that was computed correctly by the applied algorithm.

There are several metrics to determine the performance of the model and improve it if the performance is not up to the mark. This includes performing tasks like cross-validation, parameter tuning, etc. Hence, we can conclude that supervised learning uses labeled classes and target values to classify an unseen data point.

Unsupervised learning

In contrast to the supervised approach that already knows the predicted outcome, unsupervised learning uses the basis of similarity in properties to classify the unseen data points in the given n-dimensional space.

The main idea is to take a data point that is new to the given space, extract the behaviors of the data point, compare it with the already existing properties of the other objects and accordingly classify or categorize them into the appropriate group. The common examples of unsupervised learning are clustering, Apriori and K-means algorithm.

Reinforcement learning

Reinforcement learning is very similar to the animal kingdom where the animals do not train their offspring to perform a particular task but they leave them out in the ecosystem to learn from the experiences that it gains while struggling to accomplish a particular task.

The basic idea of performing reinforcement learning is to let the model learn on its own. It uses a trial and error strategy to gain knowledge from the available environment. According to the experiences gained from the conditions, it is exposed to, appropriate predictions and decisions are made. Markov Decision Process is an example of reinforcement learning.

Conclusion

Because of the wide variety of applications offered by machine learning, there are several Machine learning courses dedicated to offering the training in machine learning algorithms so that an individual can recognize the problem efficiently and work towards building an appropriate solution. Learning and understanding of machine learning algorithms are very easy. It just needs a proper classification of the interest in performing the desired operation.

How Artificial Intelligence and Machine Learning Is Altering the Marketing Landscape?

Posted on by Imarticus Learning

In this technological era, new technologies are being introduced in the market now and then. Businesses are no more using the traditional marketing methods to generate ROI (Return on Investment). Businesses are using new-age technologies like AI (Artificial Intelligence) and ML (Machine Learning) for better marketing and promotion.

Many young marketers are looking for a certification in AI & ML for securing better job roles in the marketing industry. The marketing landscape is continuously changing, and businesses need a sustainable solution. Read on to know how AI & ML are disrupting the traditional marketing landscape.

Use cases of AI and ML in marketing 

 One can see many real-life use cases of AI and ML in marketing around them. Some of the most popular use cases of AI & ML in marketing are listed below:

  • Businesses now use chatbots for their websites or software applications. Chatbots can interact with customers all day long and can solve basic queries. With a better user experience, businesses can retain their customers.
  • AI & ML can help provide accurate information to customers whenever needed. Inducing automation in marketing processes can reduce the manual load on an organization.
  • AI and ML tools are used by businesses to track dark social sharing up to an extent.
  • Many businesses are planning to use self-driving vehicles for marketing campaigns.

Driving factors for the use of AI & ML in marketing

 At present, businesses need to create personalized marketing content based on the preferences of the customer. Predictive analysis powers of AI/ML tools are helping businesses to create personalized marketing content. The recent COVID pandemic made it difficult for marketers to connect with their customers.

There was no physical interaction with the customers during the pandemic. AI & ML can help in collecting customer data even without stepping into physical markets. The need to decrease human labor in marketing processes has also forced businesses to go with AI and ML tools. With automated marketing processes, businesses can cut costs and boost ROI. In recent times, many businesses have focused on hiring marketers that have a certification in AI & ML for better results.

 How AI & ML are altering the marketing landscape 

Traditional marketing campaigns are launched with minimal insights. With AI & ML, you will have better insights before launching marketing campaigns. Marketing campaigns are becoming target-specific with AI & ML.

An ML/artificial intelligence course can help in learning more about disruptions caused in the marketing landscape. Businesses have to adapt according to the ever-changing marketing landscape to retain their market share. The benefits of AI & ML in marketing are listed below:

  • Marketers are getting better sales leads with AI & ML tools.
  • Marketers can understand the customer behavior and then launch marketing campaigns accordingly. It is better to understand the pain points of customers and then create personalized products/services.
  • Businesses are leveraging the power of customer data with AI/ML tools. The overall marketing cost is also decreased with the use of AI and ML.

How to learn AI & ML in 2021?

Young aspirants can go for the ML/artificial intelligence course offered by Imarticus Learning. The course name is ‘Certification in Artificial Intelligence & Machine Learning’ sponsored by E&ICT  Academy, IIT Guwahati. The certification in machine learning and AI follows an industry-approved curriculum. With numerous real-life industry projects and assignments, Imarticus offers a better learning experience.

Conclusion

Online certification in machine learning and AI can help marketers in securing better job offers. The role of AI & ML in marketing is increasing due to the recent WFH (Work from Home) culture. Start learning AI and ML for productive marketing!

What Makes an Imarticus – UCLA Certified Analytics & AI Professionals To Be Forever in Demand

Posted on by Imarticus Learning

Modern-day technologies like AI (Artificial Intelligence), ML (Machine Learning), data analytics, etc. are revolutionizing the working culture of businesses & firms. Analytics professionals use these technologies to ease & pace the analytics process.

Analytics professionals are required to extract meaningful insights from huge chunks of unorganized data to make better business decisions. Professionals with a certification from a reliable source surely get an edge over others when it comes to upward mobility.

Let us see how can you get the Imarticus – UCLA certification in Analytics & AI along with its benefits.    

Course Overview

Imarticus provides a post-graduate program in Analytics & Artificial Intelligence in collaboration with UCLA Extension which is a major certificate issuing institution in the United States.

Analytics and Artificial Intelligence CourseYou will get dual certification from two reputed sources if you opt for this course. Many individuals have built their Analytics and Artificial Intelligence careers successfully with the help of this course.

You will be receiving live training from world-class celebrities from UCLA & Imarticus. This course has around 400+ hiring partners with a 3-30 lakh salary range. You should have completed your graduation with at least 60% marks to enroll in this course.

Let us see how Imarticus – Analytics & AI professionals are forever in demand.

Broad Course Contents

This 28 weeks long online course covers all the aspects of analytics & AI along with data science fundamentals which helps in building a successful career. You will get to know about various tools/languages via this course like Python, Scikit-learn, Keras, TensorFlow, NLTK, OpenCV, etc. The major topics which will be covered in this course are as follows:

  • Data Science Fundamentals – You will be introduced to statistics in the first four weeks. You will also get to learn Python basics during this tenure. After that, you will perform data analysis with Python. Various concepts of data processing & statistics like central tendency, standard deviation, Z-score, etc. will be taught to you.
  • Machine Learning – ML will be taught to you for 6 weeks. Various topics of machine learning like multiple regression, correlation analysis, dummy variables, etc. will be taught to you. You will also be able to run various ML models in Excel in the practical classes. You will also learn about data science business models developed with the help of machine learning.
  • Deep Learning – Deep learning is a cutting-edge technology used for forecasting & enhancing decision-making ability. You will be taught about deep learning with an industry-first approach for 6 weeks.
  • NLP – Natural Language Processing (NLP) has helped in enhancing the ways humans react with computers. It has also automated the analytics process in firms/organizations. You will be studying this topic for about 4 weeks.
  • Computer Vision – You will be taught about computer vision, an AI-based technology that helps computers in extracting information for digital images and videos. You will focus on this topic for around two weeks including practical classes.

Pros of the Course

Besides the gravity of certifying institutions, the teaching methodology of this course ensures that the students will have a long run in the industry.

 

Analytics and Artificial Intelligence CourseThe pros of opting for this Imarticu’s Analytics and Artificial Intelligence course are as follows:

  • This instructor-led training is self-paced so that you can take your time to understand concepts.
  • You will work on various in-class industry-oriented projects to know about the practices in the industry.
  • You will also come across practice projects, boot camps, capstone projects, workshops, etc. if you opt for this course.
  • You will also get to test yourself in a hackathon at the end of the course.
  • The last two weeks of the course will be dedicated to placement preparation where you will get to learn from industry experts. Imarticus also provide excellent placement support to its students.
  • You will be provided a dedicated program mentor to guide you through the course and also for career advice. You can also monitor your test results & course progress.Analytics and Artificial Intelligence Course

Conclusion

This course follows an industry-first approach to make you ready for the industry. You can build a successful career with the broad topic coverage & placement support of this course.

Analytics and Artificial Intelligence Curse

Enroll in the Imarticus – UCLA Analytics & AI course now!

How Do You Become A Machine Learning expert?

Posted on by Imarticus Learning

Machine learning is a powerful tool used to transform information into knowledge. Over the past few decades, the world has witnessed a surge in the data which is useless unless it is analyzed and used effectively. Machine Learning makes this easier. By training machines to analyze the data sets and their patterns and behaviors, data science helps create an environment super adaptable for the organizations that need help with data every single day.

There are several methods to master Machine Learning and Machine Learning Certification is one of them. Imarticus Learning offers Machine Learning and Deep Learning Prodegree. 

This course helps the learners in the following ways:

Learners Can Gain Job-Relevant Skills

Along with building skills in Machine Learning, Python, deep learning, and IBM Watson through 16 industry and in-class projects, candidates get an intense Capstone project.

Smooth Career Transition

Imarticus has witnessed more than 1400 successful career transitions among its learners. This was possible due to the career support module which comes within the program, enabling a smooth transition into ML and AI fields.

Receive Industry Certification

The machine learning certification awarded by Imarticus is endorsed by IBM, a global leader in providing solutions that are technology-driven.

Take Advantage of Online Physical Classrooms

The classes are Live. They come with facilitated instruction for best learning outcomes. The learners can learn while interacting with the instructors, while gaining a full-scale knowledge online.

What is Differential Deep Learning?

Posted on by Imarticus Learning

Introduction

The world today has changed drastically. Therefore, it is important to change according to the environment in which we function. One such major change has been brought about by the advent caused by Artificial Intelligence.

Deep learning is a concept which has come around from artificial intelligence. It has been adopted by a lot of companies who want to automate their processes and make things easy. Also, people have started transitioning to deep learning to cut their losses and maximise profits. Therefore, deep learning is creating a lot of jobs in the market.

So, you can take up a Deep Learning course to increase your employability.

Understanding Differential Deep Learning

Deep learning is a way of implementing mathematical methods in the day to day working of a company. Numbers change every day. In most cases, we have to do these changes manually. With deep learning, you can keep a tap on the numbers without any additional effort.

With Deep Learning, you can formulate different algorithms to make your job easy. Deep Learning provides you with the ease of making predictions about future rates and prices based on the past and current trends and also on the market situations at different points of time. It provides ease of calculating and predicting values even if the market is volatile. Deep learning increases the analytic speed and efficiency of any company.

With proper Machine Learning training, you can design different algorithms for a different set of problems with which the company deals. Application of Deep Learning brings in automation which drastically improves the performance of any system is it calculating the sales value, keeping a tap on the generated leads, making a financial analysis, etc.

Differential Learning principals can be applied to different machine learning algorithms. Also, once you are well versed with the working of differential learning, you can try your hands on any machine learning language. It is going to be easy for you to understand.

You can apply Differential learning in models like regression, Principal component analysis, etc to get the best results. Also, differential deep learning is effectively used in the field of Finance. This can be used to develop a mechanism on pricing.

You can also use this to manage and assess various risks. Differential deep learning is effectively used to produce different models for closed-form solutions. Various Risk management metrics like hedge strategies, etc can be easily generated with the help of deep learning.

Uses of Differential Deep Learning

  1. It is used for developing different machine learning models where these algorithms are built to give results on different sets of inputs.
  2. Differential deep learning is being widely used in the field of risk management. With the use of different models, you can assess the situation at different values,
  3. It makes use of Automatic Adjoint Differentiation which can calculate changes and derivative sensitives quite easily.
  4. With Differential deep learning various fast pricing analytics can be formed which can be used to compute the metrics of risk management at different sets of information
  5. It is used to extract important estimations and values even when small data sets are available. Using differential deep learning can bring you to a conclusion very easily.
  6. It replaces the frequent interference of humans which was a prerequisite earlier. Differential Deep learning creates a system which is automated and reduces the cost of labour to a great extent. This system reduces the chances of errors drastically.

Conclusion

Differential Deep learning has changed the way we use our data. It has made the application of Machine learning possible. Also, with the help of this, companies are moving towards a fully automated world.

How the Indian Government’s AI Website Will Help the Country’s Artificial Intelligence Industry?

Posted on by Imarticus Learning

Collaboratively developed by the National Association of Software and Service Companies (Nasscom) and the National e-Governance Division of the Ministry of Electronics and Information Technology (MeitY), the portal is intended to be a one-stop-shop for all AI-related developments to happen in India.

The portal will also distribute documents, case studies, research reports and more relating to AI, and will also become a repository for AI-related learning job roles.

Noteworthy Resources On the Website

  • AI Courses

In order to help professionals and fresh graduates upskill, a variety of AI-related and machine learning courses are being offered through this government portal. The course providers range from IIT Bombay to companies such as IBM.

  • Editorial content

In order to collect all related information in one place, the website publishes editorial content and news as well as case studies and resources for budding AI entrepreneurs.

  • Highlighting successes

The portal also aims to highlight the country’s successes in AI in the recent past, spanning industries and use cases.

How the Website Will Help India’s AI Industry

The website is a distinctive step forward in recognizing the potential and scope of AI in India. Here are some ways in which the website can benefit the AI landscape at national and grassroots levels:

  • Democratising AI

Sanjay Dhotre, the MoS for E&IT, Communications, and HRD, said that digital innovation, including AI, has been a massive equaliser among the Indian population despite the diversity of the country. AI has already penetrated several homes; in the future, this website hopes to bring AI to virtually every part of the country and expound on its benefits in a growing economy.

  • Cementing India’s Place in the World

This public declaration of support towards Artificial Intelligence developments is a solid way of confirming to the world that India is investing in the technologies of the future. India has already supplied global leaders in technology to different parts of the world. By funding this AI website and encouraging its use, the country is making its way up the list of countries with one eye on the foreseeable future.

  • Encouraging Innovation

By making case studies, courses and resources accessible to the masses, the AI website is, in effect, encouraging each and every citizen to experiment in the field. There is no dearth of innovation and ideation in the country; this website becomes a place for resources to support the realization of AI-related dreams and the furthering of the country’s technological goals.

  • Building Networks

The website is touted as a convergence point for students, fresh graduates, AI experts, experimental businesses and new-age entrepreneurs to share and transact over revolutionary AI-driven ideas and proposals. The website also becomes a launching pad for innovation and a networking platform for those with ideas and those with the power to fund them. The departments are also working on creating investor pitch events to link ideators and funders alike.

  • Preparing the Future Generation

Alongside the website, the departments also announced the Responsible AI for Youth Program which is targeted at imparting AI education in government schools. The different phases of the programme aim to train teachers and gather groundbreaking ideas from the students themselves. Keeping in mind that India is one of the most populous countries in the world, instilling an understanding of AI and a passion for innovation at a young age is critical to the growth of the country internationally.

Conclusion

The AI website can be looked at as a show of support towards futuristic technologies that emerge from the grassroots level. By attempting to democratize AI, this government website is also creating a conducive atmosphere for technological growth on par with the rest of the world.

Solve Real-world Text Analytics Problems With NLP!

Posted on 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

data analytics courses

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.

Natural Language ProcessingHere 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. 

Natural Language ProcessingSocial 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.