Getting started with any machine learning project often starts with the question: “How much data is enough?”. The response depends on a number of factors like the diversity of production data, the availability of open-source datasets, the expected performance of the system, and the list can go on for quite a while. In this article, I’d like to debunk a popular myth about machines only learning from large amounts of data, and share a use case of applying ML with a small dataset .
With the rapid adoption of deep learning in computer vision, there are more and more diverse…
As technology advances and more organizations are implementing machine learning operations ( MLOps), people are looking for ways to speed up processes. This is especially true for organizations working with deep learning (DL) processes which can be incredibly lengthy to run. You can speed up this process by using graphical processing units (GPUs) on-premises or in the cloud.
GPUs are microprocessors that are specially designed to perform specific tasks. These units enable parallel processing of tasks and can be optimized to increase performance in artificial intelligence and deep learning processes.
GPU is a powerful tool for speeding up a data…
Like many other revolutionary technologies of the modern day, machine learning was once science fiction. However, its applications in real world industries are only limited by our imagination. In 2021, recent innovations in machine learning have made a great deal of tasks more feasible, efficient, and precise than ever before.
Powered by data science, machine learning makes our lives easier. When properly trained, they can complete tasks more efficiently than a human.
Understanding the possibilities and recent innovations of ML technology is important for businesses so that they can plot a course for the most efficient ways of conducting their…
Data powers machine learning solutions. Quality datasets enable training models with the needed detection and classification accuracy, though sometimes the accumulation of sufficient training data that should be fed into the model is a complex challenge. For instance, to create data-intensive apps, human annotators are required to label a huge number of samples which results in complexity of management and high costs for businesses. In addition to that, there is a difficulty of data acquisition related to safety regulations, privacy, or ethical concerns.
When we have a limited dataset including only a finite number of samples per class, few-shot learning…
We wondered whether it was possible to apply a generative model for a problem of content synthesis in order to automate the work of graphic designers. So we focused on logotype synthesis and checked this hypothesis. Our aim was to find out the possibilities and limitations of the technology, as well as the prospects of its application for the generation of images.
To create unique icons and explore how the image generation actually works with generative models, we used one of the most powerful generative architectures — StyleGan2. …
Google continues to reveal the details of Android 12, and so far we’ve seen two betas released in May and June. Material You, a new Google material design language that is a great part of Android's upcoming update, has been getting a lot of buzz in the digital community.
“We rethought the entire experience, from the colors to the shapes, light and motion, making Android 12 more expressive, dynamic, and personal. This work is being done in deep collaboration between our software, hardware and Material Design teams, and we’re unifying our software and hardware ecosystems under a single design language…
Since 2004, Airbnb, GitHub, Zendesk, Shopify, Kickstarter, UrbanDictionary, CrunchBase, and hundreds of amazing products, including yours, have been built with Ruby on Rails.
Is Ruby on Rails dead in 2021? The language has recently offered some reasons for developers to consider migration to different tools.
Can a machine attempt to approach the task of creating unique content that would be indistinguishable from human-produced artifacts? Is it possible to do this with the help of generative adversarial networks (GANs) — by learning the structure of the complex real-world data examples and generating similar synthetic examples that are bound by the same structure? With the recent advances in the development of generative models, the answer seems to be yes, at least to an extent. It is proved by existing GAN applications.
In this article, we are going to take a deep dive into what the generative models…
According to 2020’s McKinsey Global Survey on artificial intelligence (AI), in 2020 more than 50% of companies have adopted AI in at least one business unit or function, so we witness the emergence of new AI trends. Organizations apply AI tools to generate more value, increase revenue and customer loyalty. AI leading companies invest at least 20% of their earnings before interest and taxes (EBIT) in AI. This figure may increase as COVID-19 is accelerating digitization. Lockdowns resulted in a massive surge of online activity and an intensive AI adoption in business, education, administration, social interaction, etc.
This article aims…
One of the most important things that healthcare software developers need to adhere to is the Health Insurance Portability and Accountability Act (HIPAA). This law protects private health information. Anyone who operates or invests in medical businesses knows about it, but failure to follow its rules correctly can result in very unforgiving consequences. Last year, millions of dollars in fines were issued due to HIPAA information privacy breaches. How can you ensure that your product is compliant with HIPAA?
There’s a good reason why these measures are in place. Rising demand on black market dark websites for valuable healthcare information…
