In 2011, AlexNet’s achievement on a prominent image classification benchmark brought deep learning into the limelight. It has since produced outstanding success in a variety of fields. Deep learning, in particular, has had a significant impact on computer vision, speech recognition, and natural language processing (NLP), effectively reviving artificial intelligence. Due to the availability of extensive datasets and good computational resources, Deep Learning has even prospered to a whole new level. Although massive datasets and good computational resources are there, things can still go wrong if we cannot optimize the deep learning models properly. And, most of the time, optimization seems to be the main problem for lousy performance in a deep learning model. The various factors that come under deep learning optimizations are normalization, regularization, activation functions, weights initialization, and much more. Let’s discuss some of these optimization techniques.
Artificial Intelligence (AI) is the intelligence illustrated by machines that helps massively in our world today. It helps modify our productivity, supplements what we do, and delivers tasks that a human cannot. Machine learning (ML) is one of the most important subsets of AI and is also considered its most applicable subset. It teaches a computer to learn and process data without human interference. Arguably, it’s now becoming a vast source of data support, aiming to help achieve better results. With how modernized the world is today, it can’t be denied that AI, specifically, ML is revolutionizing different industries. One of the industries that now rely on ML heavily in healthcare. Machine learning undoubtedly plays a significant role in health care, from medical data gathering to the development of medical procedures and treatment of some chronic diseases.
As technology advances, more and more people are focusing on doing their businesses online. As a result, this has led to the growth of data science. Technologies like Machine learning (ML) enable businesses to access insights from raw data and use it to solve various business problems. Likewise, integrating ML text analysis with an existing business process ensures that the business is always up to date with business and consumer needs. Here is a list of key ways in which machine learning Text Analysis can help your business. You can see this link https://goascribe.com/machine-learning-text-analysis/ for more information.
Test automation is an exciting field of software testing. More than 20 years of automation testing innovations have transformed how development teams produce, release, and maintain software applications. For testers, automation means no more dealing with boring and repetitive tests. For developers, it means not having to wait very long for feedback and release status. For project managers, automation means no sub-par application sees the light of day. Development teams pursue test automation in the hopes that it can make their work easier.
A technology professional never stops learning because there’s always something new around the corner. Adding to your skills brings immense rewards, from boosting income to improving prospects and bringing satisfaction. But you need to choose the skills wisely because learning takes time and effort. While personal aptitude should matter, do not forget to pick the ones in demand. These are worth investing in because they enhance your marketability as a professional. Here are the top in-demand skills techies must consider exploring in 2021.
The term ‘machine learning (ML) pipeline’ refers to the most efficient methodology for creating a machine learning model. It comprises multiple steps, and substantial quantities of data before deployment become a possibility – and, of course, for interventions to be made by the most central component to developing artificial intelligence: the human being. Read more below.
All over the world in boardrooms great and small can be heard talk of artificial intelligence and machine learning. AI/ML have become the short form way of writing about it because the words are used so often, it actually reduces productivity to write it out. They are no longer just words, they are buzzwords, power words. They will be mentioned in every official communication from every company. It doesn’t matter whether the subject is manufacturing or the driest pages of a quarterly report. CEOs are extremely concerned about whether their machines are learning. That’s fine as far as it goes. Machine learning is powering a lot of what the company will be doing for the foreseeable future. However, one suspects that some executives used the terms without having a strong grasp of what they really mean. No one wants to seem like they are not keeping up with the latest trends. But not every machine needs some form of artificial intelligence to be useful. No one wants an artificially intelligent hammer that makes decisions about whether or not it will pound nails. Consider these machines that are intelligent enough just the way they are:
Machine Learning: The use and development of computer systems that can learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Artificial Intelligence: Artificial intelligence (AI) is the computer’s ability or a robot controlled by a computer to do tasks that humans usually do because they require human intelligence and discernment. Machine Learning is a precursor which helps create use cases for Artificial Intelligence such as Intrusion Detection, Image recognition, or categorizing data. The combination of both ML and AI has a great potential to shape the future of all technological innovations.
Would you believe it if I tell you that you are a part of one of the sources generating big data? A layperson may instantly disagree, but it is, in fact, valid for most people using smartphones. Whether you are using social media, sending money to someone online, or using a variety of mobile applications, all of your web activity leads to data generation. This data is precious to companies who want to understand their customers better and provide personalized services. For example, eCommerce giant Amazon uses your shopping history and past product search data to recommend you related products you are most likely to buy. Research shows that we generate around 2.5 quintillion bytes of data each day, which is quite huge! Well, you may wonder how can such a massive amount of data be handled. This is where Hadoop comes into the picture. Apache Hadoop is one of the earliest open-source tools to offer storage and large-scale processing of big data. As mentioned in Apache.org, Hadoop is a framework that allows for distributed processing of large data sets across clusters of computers using simple programming models. Various companies and organizations use Hadoop for research as well as production.
Do you have a data science interview coming up and want to know how to prepare for it? If so, you have already made some progress. Getting an interview means you have gone through the initial stages of the hiring process. This includes applying for a job and submitting your resume. You may also have taken a technical and aptitude test. Given that you have been selected for the interview, you would want to be well prepared to impress your potential employer. Below are some tips to help you crush your data science interview and land the job you aspire.