Future of Deep Learning _ Where are we heading towards

May 11, 2021

What is Deep Learning?

In simple terms, Deep learning is a subset of artificial intelligence, focusing on making robots learn what humans naturally do - learn by experiences. In deep learning, machines learn-to-learn with the help of data sets. Deep learning algorithms use artificial neural networks to analyze data as the human brain does independently. Of course, the data training, humongous knowledge base, and pattern recognition techniques are fed to the machine by humans to work on their own later.

Some examples of using deep learning to replace manual work are, Voice commands to phones or laptops, Driverless cars, Face recognition, Text translations, and Sentiment analysis.

Why Deep Learning?

Now that we know the meaning of deep learning, the question arises - why would we want machines to behave like humans? Experts have given several answers to this question, and out of those some are: “for diminishing mundane, repetitive work”, “ To fasten the work speed”, “To achieve accurate results in strict timelines” But the most important reason for exploring branches of advanced concepts of deep learning is “Accuracy.” Deep learning has improved the level of accuracy many times. Multiple tasks like car driving, printing, text recognition, and natural language processing are done more accurately than previously with deep learning. Deep learning has outperformed human minds in computer vision consisting of classifying objects in any image.

Although the term “Deep learning” was introduced by distinguished professor Rina Dechter in 1986 but became a shining term recently due to accelerating demand for less time consuming and accuracy driven services and products, to achieve these demands in a competitive market by businesses, deep learning acted as the magic tool. It became useful by giving solutions when:

  1. Solutions required a large number of data sets, majorly labeled data. For example, To develop driverless cars, the development team would require to process millions of pictures and gigabytes of videos parallel.
  2. Solutions required high throughput or computation power. High-performing deep learning GPUs are connected in parallel for deep learning algorithms as it requires a high amount of data to be processed in less time. These deep learning models are connected with cloud computing clusters to enable developers to process data quicker. This reduces training time for machines with loads of data transferred into their knowledge base. Even though high throughput machines are used for processing, it may take weeks to train a machine due to its complexity.

How Does Deep Learning Work?

Deep learning algorithms, also known as “deep neural networks” use a neural networks to function. Neurons in the neural networks work on similar lines with neurons in the human brain. Neural networks architecture consists of nodes arranged in a layered fashion. More the layers, the more precise and elaborated your model would behave. Deep neural networks contain a very high number of layers which can go up to 150 or more.

In the networks, the node sends out the signal from one node to another and assigns weights to it. The nodes with heavier weight have a greater impact on associated layers. The layers are arranged in a sequence. The final layer compiles weighted inputs and generates output.

To understand the working of deep learning, let us take an example.

Problem statement: Deep learning algorithm receives a Cat’s image as an input and outputs “yes” if there is Cat in that image otherwise “no”.

Solution by deep learning algorithm:

Not all cats look alike. Their different colors, sizes, angles of pictures, light density, image quality, and object shadows add to the complexity of determining cats from the image. Hence, the training set should include multiple images covering the cat’s maximum determining characteristics in an image. Many examples of cat images must be included, which could be considered “cat” by humans and also images that can not be categorized as “cat” images should be included. These example images are fed in the database of neural networks and stored in data sets. This data is mapped into the neural networks; nodes assign weightage to each data element. Output compiles all the disconnected information to conclude. If the algorithm finds out that the object in an image is furry, has four legs, has a tail, then it should be “cat”. There are hundreds of characteristics like this which are particular to cats defined in trained data sets to distinguish them from other objects.

The answer received after all the analysis mentioned in the above paragraph is then compared with the human-generated answer. If these two answers match, then the output is validated and saved as a success case. In case of the answers mismatch, the error is recorded, and weights are changed. This process is repeated several times, and weights are adjusted multiple times until we attain high accuracy. This type of training is known as “supervised learning”. The machine is trained until a point is reached where machines can self learn with previous examples.

Challenges in the future of Deep Learning

  • Massive datasets: Massive amount of datasets is a challenge in deep learning with increasing data day by day. Here we are talking about training data. Humongous data is scattered in the market with no detectable pattern as an input data set, but the same data set should be arranged while used for training purposes. It becomes a tough task to find different types of example datasets to maximize example training data coverage. New concepts of generative adversarial learning and transfer learning are used to overcome this challenge.
  • Overfitting: “The greatest danger in art is too much knowledge”. Overfitting is a common problem in deep learning when data analysis focuses too closely on the dataset. In this situation, even the mundane and non-important parameters are recorded, which results in skewed results. Due to the highly receptive nature of neural networks, accurate results depend upon correct characteristic determination. Similarly, the chances of deviating from results are also there if the algorithm focuses on the wrong characteristics. But this issue can be overcome by adhering to the right data sets. One of the famous research papers “Dropout: a simple way to prevent neural networks from overfitting” is a great knowledge source for researchers and scientists to reduce overfitting related errors.
  • Privacy breach: It is noted that data privacy has gained notion in recent years. Recently, the Federal trade commission (FTC) snapped a $5 billion penalty on Facebook for mishandling the data and privacy of application users. Deep learning is a data-dependent technique where data is recorded on a wide-scale to achieve accuracy in results. But due to several privacy laws and restrictions, it has become challenging to gain access to critical data which prevents deep learning from reaching accurate results.
  • Butterfly effect: Deep learning is vulnerable to produce outrightly inaccurate results even if there is the slightest change in the input data. It makes any algorithm unstable and unreliable for mission-critical or decision-making applications. The instances have been recorded where hackers can add the unnoticeable amount of “noise” in the data set to completely corrupt the result.

How is Deep Learning Beneficial in the Future?

  • Self-learning or ubiquitous deep learning: As of now, most robots still require human assistance to learn new situations and reactions. But deep learning can help in designing models via which robots can learn themselves. This would help various businesses that are not experts in AI but still can take advantage of self-learning bots to reduce the number of human errors and increase the speed of transactions.
  • Deep learning for cybersecurity: Security incidents have also risen parallelly with advancements in technology. The list of attacks is pretty long with some famous ones like WannaCry, Capitol One breach, NotPetya. It has become a necessity for businesses to act fast and proactive to prevent losses from these. More Cyberdefense agencies would subscribe to deep machine learning algorithms to respond faster than humans and detect patches in IT Infrastructure to Reduce the Impact of attacks.
  • Automation of repetitive tasks: When did you visit the car garage last time? Did you try to observe several mundane tasks that could have been automated? Deep-learning robots mounted with deep learning abilities can complete tasks with input received from different AI sources and sensors. Like the way humans act based on the environment and experiences, robots can also act according to data sets containing previous examples and input data from sensors.
  • Machine vision: Deep learning has brought revolutionary changes in the way images are perceived and classified. Different actions like object detection in images, image restoration or recreation, image classification, and even detention of messages from the handwritten text can be performed using deep learning. This functionality, driven by deep learning provides an analytical vision to machines, which helps get into details that would have been upheaval tasks for humans.
  • Deep learning to enhance customer experience: Deep learning is used to create a useful application to improve your business experience. One common example which you find in almost all consumer-centric websites is Chatbot. Chatbots uses deep learning to analyze customer text and generates responses accordingly. Other examples are image captions (The complexity of image captions reduces if you cannot identify one after each attempt).
  • Deep learning in marketing: The use of websites for commercial purposes has gained traction in the COVID-19 era. Consumers are becoming smarter day by day by ordering their required products online with a click’s comfort. Similarly, businesses are also becoming smart by subscribing to smart marketing with the help of deep learning. Deep learning is outperforming humans in SEO. The real-time web content generators are used to tweak content and optimize the website for SEO. This helps websites improve their SEO ranking without the interference of human SEOs. Google has pioneered digital marketing with the help of the best GPUs for deep learning.

Deep Learning has a Bright Future!

It is predicted that deep learning would become widespread and embedded in the system to achieve faster and accurate outputs. GPU Cloud instances offered by E2E makes it easy and affordable to build and deploy deep learning systems.

As per the article by Big Data Evangelist James Kobielus in “6 Predictions for the future of deep learning”: The deep learning industry will adopt a core set of standard tools, and Deep learning will gain native support within various platforms like a spark, open analytics ecosystem with reusable components and framework libraries. The same has been indicated by “Ray Kurzweil”. He became famous for his prediction that Artificial Intelligence would outsmart humans in computational capabilities by 2029.

In a nutshell,

Deep learning models are expected to exponentially grow in the future to create innovative applications freeing up human brains from manual repetitive tasks. A few trends which are observed about the future of deep learning are:

  1. Support and growth of commercial activities over the networks. NLP and digital marketing have increased the use of deep learning algorithms and gained valuable attention from consumers.
  2. An urge to automate repetitive tasks requiring more physical labor than mental involvement will encourage data scientists and engineers to innovate in AI continuously.
  3. A tussle between data protection organizations and deep learning research agencies will prevail in the future too.
  4. The limitation of deep learning is the “ability to reason” is a bottleneck to create independent decision-making machines.

E2E Networks hopes that this article has shed light on the bright future of deep learning. For more blogs, check out the E2E Networks website.

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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. 

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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:


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. 


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


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. 


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.



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. 


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.






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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.

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