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Source: Medium

The major issue arises from the essence that machine learning algorithms were not primarily developed to deal with eloquent and dynamic adversaries, and therefore, in concept, the entire security level can be breached by leveraging the relevant security issues of mastering algorithms through meticulous tampering of raw data.

Here Adversarial learning can come into the picture!!

Adversarial learning is a novel research area that mainly lies in the convergence of machine learning and cybersecurity. It focuses on allowing the secure implementation of machine learning approaches in adverse circumstances such as spam filtering data protection and biometric identification.

What is Adversarial Machine Learning?

Adversarial machine learning…


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Source: Towards Data Science

The Cloud Native Computing Foundation (CNCF) found that in 2019 the vast majority of respondents — 84 percent — were running containers in production. That was up roughly 15 percent (or 11 percentage points, from 73) from the previous year. Production container usage was at just 23 percent when CNCF first did its survey in March 2016.

As a newbie in “Machine Learning”, it’s always an exciting journey that starts from data cleaning, exploratory data analysis, feature engineering, and it usually ends at model training and deploying it through the flask. …


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Source: DataCamp

Transfer Learning will be the next driver of Machine Learning success. — Andrew Ng

What is Transfer Learning?

The general idea of transfer learning is to use knowledge learned from tasks for which a lot of labeled data is available in settings where only a little labeled data is available. Creating labeled data is expensive, so optimally leveraging existing datasets is key.

In a traditional machine learning model, the primary goal is to generalize to unseen data based on patterns learned from the training data. With transfer learning, you attempt to kickstart this generalization process by starting from patterns that have been learned for…


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Source: Twitter

It was reported in 2020 that about 222 per 10,000 children in the United States have autism spectrum disorder, one of the highest diagnosis rates in the world. Autism spectrum disorder is more prevalent in boys than girls. In the U.S., about 3.63 percent of boys between 3 to 17 years have autism spectrum disorder equivalent to 1.25 percent of children.

What is Autism?

Autism Spectrum Disorder (ASD) is a range of neurodevelopmental disorders mainly distinguished by decreased cognitive interaction and speech deficiencies.

Symptoms of Autism:

Symptoms can include excessive concentration on one object, unresponsiveness, lack of comprehension of verbal norms (such as voice tone or…


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“Netflix has a global algorithm which is helpful in the recommendations to all the users and the company claims that the combined effect of personalization and recommendations is worth $1 Billion per year!”

Well, the above statistics are not much surprising as we know what potential the recommendation systems are having these days! More surprising are, the use-cases of Artificial Intelligence (AI)!

AI has paved its way into the film-making industry now!

Recently, a group of researchers presented their research in “The Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing” forming a deep learning model to…


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Source : Towards Data Science

According to David Kenny, General Manager, IBM Watson — the most advanced cognitive computing framework, “AI can only be as smart as the people teaching it.” The same is not true for the latest cognitive revolution.

What is Cognitive Computing?

Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. The phrase is closely associated with IBM’s cognitive computer system, Watson. Cognitive computing overlaps with AI and involves many of the same underlying technologies to power cognitive applications, including expert systems, neural networks, robotics and virtual reality (VR).

How Cognitive Computing works?

Cognitive…


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Source: Adext

Google and Facebook combined hold 84% of the market share in global digital advertising according to the Financial Times in 2017. Among the leaders in this field is Adext AI, whose audience monitoring system can improve ad investment efficiency by as much as +83% in just ten days.

What is Adext AI?

Adext AI, a framework that streamlines electronic advertisement and promotional operations using AI, prevents services from hours spent on operational and routine activities.

In today's modern environment, Adext is the first and yet the only Ad Tech Artificial Intelligence (AI) Technology that serves as a Software as a Service (SaaS).

Once subscribers…


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Source: Amazon

“Market analysts predict that more than 750 million Edge AI chips and computers will be sold in 2020, rising to 1.5 billion by 2024. And while most of these will be installed in consumer devices like phones”

To those who are following, the advancements in Machine Learning (ML)/Artificial Intelligence (AI) would be surely aware of the fact that the usage of these technologies would be predominant in the upcoming future!

While parallel to that growth, even the Internet of Things (IoT) market is also growing at a full-fledged pace, with the number of IoT devices being projected in 2025 is…


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Source: IoT Agenda- TechTarget

According to a recent report by Transparency Market Research (TMR), the edge analytics market is foreseen to project a strong growth with a noticeable CAGR (Compound Annual Growth Rate) of 27.6% within the forecast period.

What is Edge Analytics?

Edge analytics is the advanced data analysis method that enables users to get access to real-time processing and extracting the unstructured data captured and stored on the edge of network devices. Edge analytics provides the automatic analytical computation of generated data in a real-time mode without sending the data back to the centralized data store or server.

In this technique, data is collected, processed, and…


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Source: C#Corner

The origins of MLOps goes back to 2015 from a paper entitled “Hidden Technical Debt in Machine Learning Systems.” And since then, the growth has been particularly strong. Consider that the market for MLOps solutions is expected to reach $4 billion by 2025.

What is MLOps?

MLOps is intelligence that provides a bridge between data scientists and the production team. It deeply conspires in nature and designed to eliminate all the waste and make the machine learning system more scalable by providing automation and producing highly consistent insights from the ML model.

MLOps is the idea of combining the long-established practice of DevOps…

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