ANN Vs CNN Vs RNN - Exploring the Neural Networks in AI

May 6, 2022

What are the three important types of neural networks in AI that are regularly used and are mostly talked about? Let’s scroll more on how they operate, and how they are utilized.

The three most important neural networks are:

  1. ANN (Artificial Neural Network)
  1. CNN (Convolutional Neural Network)
  1. RNN (Recurrent Neural Network)

First, let's start with a quick explanation of what neural networks in AI are.

What is a Neural Network in AI?

A neural network is a network or circuit made up of biological neurons, or, in a more contemporary meaning, an artificial neural network made up of artificial neurons or nodes. Thus, a neural network can be either a biological neural network (made up of biological neurons) or an artificial neural network in AI, made of nodes (used to solve Artificial Intelligence issues). Artificial neural networks mimic biological neuron connections as weights between nodes. 

Neural Networks in AI can discover hidden patterns and correlations in raw data using algorithms, cluster and categorize them, and learn to improve over time.

However, just as different tasks need different understandings, neural networks are no exception. While a certain type of neural network might outperform for a specific type of problem, it may even underperform if applied to some other type of problem.

That is why the classification of neural networks is done, to optimize the results by using the right neural network for different use cases. 

ANN vs CNN vs RNN-

There are hundreds of neural networks available to handle issues throughout many domains. In this section, we'll go through the classification of neural networks as ANN vs CNN vs RNN. 

ANN Artificial Neural Network- 

ANN learning has been effectively used to learn real-valued, discrete-valued, and vector-valued functions containing challenges such as analyzing visual scenes, voice recognition, and learning robot control techniques. 

ANNs send data in one direction, passing it through multiple input nodes until it reaches the output node. Due to this, ANNs are also known as Feed-Forward networks. Hidden node layers may or may not exist in the network, making its operation more understandable and making ANNs one of the most basic neural network versions.

In ANNs, a problem might have numerous instances, each of which is represented by a set of attribute-value pairs. ANNs used to solve issues with a target function output can be discrete, real, or a vector of many real or discrete-valued properties. ANN learning methods do not have an issue with noise in the training data. There may be faults in the training samples, but they will have no effect on the final result. It is commonly utilized in situations when a quick assessment of the learned target function is necessary. The total number of weights in the network, the total number of training instances evaluated, and the settings of different learning algorithm parameters can all contribute to extended training durations for ANNs.

What should ANNs be used for?

The ANN is employed in technology that focuses on difficult issue solving, such as pattern recognition challenges. 

Here are several examples: 

  • For business intelligence, predictive analysis is used. 
  • A speech-to-text transcription program that converts spoken words into text. 
  • Recognition of handwriting and facial expressions. 
  • Email spam detection.
  • Forecasting the weather.

Limitations of ANNs-

  • ANNs are capable of working only with numerical data. Before being brought to ANN, problems must be transformed into numerical values. 
  • Experience and trial and error are used to create the ideal network structure as the structure of artificial neural networks are determined by no explicit rule. 
  • The trust in ANN is low as when ANN provides a probing answer, it does not explain why or how it was chosen.

CNN (Convolutional Neural Network)

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning system that can take an input picture, assign relevance (learnable weights and biases) to different aspects in the image, and distinguish between them. Or in other words, the CNN's job is to compress the pictures into a format that is easier to manage while retaining key components for generating a good forecast 

This neural network computational model employs a multilayer perceptron variant and includes one or more convolutional layers that can be linked or pooled altogether. These convolutional layers provide feature maps that capture a portion of an image, which are then split down into rectangles and routed to nonlinear processing.

The first ConvLayer is in charge of collecting low-level details like edges, color, gradient direction, and so on. The design changes to the High-Level properties as well with the addition of layers, giving us a network that understands the photographs in the dataset in the same manner, as we do. 

A CNN requires substantially less pre-processing than other classification algorithms. Whereas simple approaches require hand-engineering of filters, 

With adequate training, CNNs can learn these characteristics.

What should CNNs be used for?

The most productive use for CNNs is image classification, for eg:- Labeling hand-written letters and digits or identifying satellite images that contain roads. 

Limitations of CNNs

  • CNNs do not encode the position and orientation of objects, therefore if the pictures have a degree of tilt or rotation, CNNs have a hard time categorizing them. 
  • Inability to be spatially invariant with respect to the supplied data. 
  • Coordinate frames, which constitute an essential component of human vision, are not present in CNNs. 
  • A ConvNet requires a large dataset to process and train the neural network.

.

RNN(Recurrent Neural Network)

RNNs make use of sequential data, such as time-stamped data from a sensor device or a spoken speech made up of a series of words. Unlike standard neural networks, a recurrent neural network's inputs are not independent of one another, and each element's output is reliant on the computations of the elements before it, the output from the previous phase is supplied into the current stage as input. 

RNNs have a "memory" that stores all information about the calculations. Because it delivers the same result by doing the same job on all inputs or hidden layers, it uses the same parameters for each input. Unlike other neural networks, the complexity of the parameters is reduced.

RNNs are utilized in applications such as forecasting and time series analysis. With recurrent neural networks, even convolutional layers are used to extend the effective pixel neighborhood.

What should RNNs be used for?

RNN can produce pretty exact predictions since it has internal memory. Furthermore, it may be used to solve problems with sequential data. 

In light of this, RNN applications include: 

  • Prediction issues Automated translation 
  • Speech recognition 
  • Analysis of public sentiment 
  • Forecasting stock prices 
  • Text generation and language modeling

Limitations of RNNs

  • The problem of disappearing or exploding gradient RNNs can't be stacked. 
  • RNNs are recurrent, which means that training them will take a long period. 
  • When compared to feedforward networks, the overall training pace of RNN is rather slow. 
  • It's more difficult to process longer sequences.

The Crux

So, what are you working on?

If you're just getting started with Machine Learning, it's critical to understand and identify the problem you're attempting to address.

Remember: 

  1. Artificial Neural Networks (ANNs) are useful for resolving complicated issues. 
  1. CNNs (Convolutional Neural Networks) are the most effective way to solve computer vision issues. 
  1. RNNs (Recurrent Neural Networks) are capable of processing natural language.
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This is a decorative image for Project Management for AI-ML-DL Projects
June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.

 

The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.

 

In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.

 

Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:

Wiom

Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 

TechVantage

As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online

Manthan

As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 

NetraDyne

Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.

 

Helpshift

Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 

Facilio

An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.

 

In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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