AI & Machine Learning

Regression models are very useful and widely used in machine learning. However, they might show some problems when comes to measure the goodness of a trained model. Their performance can be measured in many different ways. This article shows you some methods to calculate the goodness of a regression model. Though there are many possible ways to measure it, these simple techniques can be very useful in many situations and easily explainable to a non-technical audience.

With the advancement in AI technology, there are many initiatives taken by individuals and organizations that help to deploy AI models as microservices. These individuals and organizations take care of managing all the microservices in a container efficiently. For instance, it is crucial to start the functioning of the right containers at the right time and make them communicate with each other so that developers can manage and handle memory considerations. But, if a business is using microservices architecture to deploy AI models, then the failure of a single service will not drastically affect other services of the model.

Artificial intelligence is experiencing a scorching summer mainly thanks to advances in deep learning and artificial neural networks. To be more precise, the renewed interest in deep learning is largely due to the success of convolutional neural networks (CNNs), a neural network structure that is especially good at dealing with visual data. Early work in computer vision involved the use of symbolic artificial intelligence, software in which every single rule must be specified by human programmers.

How to build good Machine Learning applications? There are many aspects of delivering a professional data science project. This article highlights the most useful tools to design, develop, and deploy full-stack Machine Learning applications — solutions that integrate with systems or serve human users in Production environments. There are many tools out there; the possible combination is infinite. There is no perfect setup. It all depends on your needs and constraints. So pick and choose accordingly.

For anyone interested in pursuing a career or research in Machine Learning and Data Science, the day will come when it is time to move beyond the python libraries to follow curiosities into the math behind it all. This will typically take you to the vast, open collection of papers detailing how it all works. The deeper you go to understand the core mathematics, the closer you may come to the flash of insight to create a new method.

The more we augment neural networks, the more we learn how to use our brains to their fullest potential. Albeit in a very abstract way, we may argue that this is the first step in linking human minds to computers. In other words, we just devised the least invasive form of brain-machine interface we could possibly think of. The relationship between neurotechnologies and artificial neural networks (ANN) is becoming progressively more bi-directional.

The web hosting industry is undergoing numerous changes, due to advances in machine learning. A growing number of companies are relying on TPUs to optimize the machine learning processes that their online applications depend on. Hosting providers are making sure that these new services are available to their customers, so they can fully take advantage of the capabilities of machine learning. How can developers go about using tensor processing units for the applications they intend to host online? There are several approaches that they can follow.

Before starting any project ideation work, you first need a solid foundation. You will only be in a position to find AI Projects if you understand your company’s and AI’s capabilities. Understanding a business can be challenging for external consultants, as they need to figure out the quirks of a company rapidly. Yet it’s continuously important for experienced employees. This post introduces you to a framework of coming up with AI Project ideas. It focuses on the foundation of thinking about AI Projects, the project-generation framework, and the team to find AI Projects. 

From analyzing huge amounts of data in a matter of seconds to provide optimized treatments to patients, to spotting even the most minuscule detail in medical imaging, AI is changing the way humans experience medicine in a lot of ways. Smart machines can’t help us become smarter (yet), but they’re assisting us with their intelligence to hopefully help us live longer, better and ultimately healthier lives. Real-world AI use cases show AI and ML are essential for many, if not all, healthcare organizations’ future. Have a look at the most interesting  AI use cases in healthcare.

Artificial intelligence for IT operations (AIOps)  refers to the spectrum of AI capabilities used to address IT operations challenges–for example, detecting outliers and anomalies in the operations data, identifying recurring issues, and applying self-identified solutions to proactively resolve the problem, such as by restarting the application pool, increasing storage or compute, or resetting the password for a locked-out user. With AIOps, there is the potential for achieving scale and efficiencies.  Such benefits can certainly move the needle for a company, especially as IT has become much more strategic.

While automating tasks that were once carried out by human workers has been a growing trend for a number of years, it’s set to experience a renaissance. One of the top strategic technology trends for 2020 is hyper-automation, which takes automated processes to the next level.Hyper-automation encompasses the totality of a business’s automation network under a single umbrella, meaning that not one, but many, automated technologies work in congruence to augment or replace human capabilities.