Hackathons could be very intimidating and stressful. The key to getting better is doing simple projects. In this article, we will look at an example of a web app that can be used for sharpening your skills when you prepare for a hackathon. We will use a powerful Google API based on Machine Learning and apply the following technologies: ASP.NET, HTML, Docker, Heroku, and Git.
In the previous article we refreshed our memory of WebRTC CDN and the ways this technology helps to minimize latency for WebRTC streams. We also discussed why load balancing and autoscaling wouldn't be amiss in CDNs. Here are the main points from the article:
The vast majority of IT specialists in various fields strive to perform manually as few actions as possible. I won't be afraid of the loud words: what can be automatized, must be automatized!
Let's imagine a situation: you need to deploy a lot of servers of the same type and do it quickly. Quickly deploy, quickly undeploy. For example, to deploy test rigs for developers. When development is carried out in parallel, you may need to separate the developers, so they don't impede each other and possible errors of one of them don't block the work of the others.
There may be several ways to solve this problem:
These days, video streaming mobile apps have been witnessed with an increased spike of subscribers & now much video content is being much cherished without any interruptions via a streaming app.
This is duly because catering to the prerequisite needs of end-users have popularized several VOD streaming services that has bought keen interest as compared to to the big black box which is slowly diminishing its presence.
According to recent forecasted data, there is a positive anticipation of the entire VOD streaming platform development’s market to expand its size & peak its value by $842.93 billion by the year 2027! Source: Softermii
The new issue of DataScienceDigest is here!
Machine learning in healthcare, the top 10 TED talks on AI, fraud detection in Uber, DatasetGAN, Text-to-Image generation via transformers, and more…
As you know, YouTube doesn't have a feature for capturing an RTSP stream, but we would like to change this and help YouTube to make their viewers happy.
Nowadays a lot of product managers have to confirm most of their decisions with AB-tests. Yet, it is far not always clear how to choose the parameters for the test. A particularly difficult parameter to tune is often the level of statistical significance. If we choose too high level - tests will fail even though improvements do exist. If we choose too low level - we'll be getting lots of "confirmations" of false improvements.
When we make decisions based on AB-tests, once in a while we'll be making mistakes. We can limit the losses caused by such mistakes by choosing the appropriate level of statistical significance.
New issue of DataScienceDigest is here! OpenAI is launching a $100 million startup fund, Albumentations 1.0 has been released, lessons on ML platforms, image cropping on Twitter, and more.
The new issue of Data Science Digest is here! Hop to learn about the latest news, articles, tutorials, research papers, and event materials on DataScience, AI, ML, and BigData. All sections are prioritized for your convenience. Enjoy!
The previous year has been very distressing for businesses and employees. Though, software development didn’t get so much affected and is still thriving. While tech expansion is continuing, Java development is also going under significant transformation.
The arrival of new concepts and technologies has imposed a question mark on the potential of Java developers. From wearable applications to AI solutions, Java usage is a matter of concern for peers.
Moreover, it is high time that developers enhance their skills as to the changing demands of the industry. If you are a Java developer, surely you too would be wondering what I am talking about what things you should learn.
We are increasingly aware of the importance of our personal data. Primarily due to numerous data leaks and the fact of numerous sales of personal information on the black market. Yes, huge corporations like Apple or Samsung prioritize the preservation of sensitive user data. However, they find it difficult to store and use them at the same time. That is why blockchain technology is the perfect tool for solving the online privacy problem.
There are constant news in the media about the problem of personal privacy, which is represented by constant data leaks and the general technological illiteracy of the world population. In the Pew study, nearly 80% of respondents said they are very concerned about how companies are using the data they collect. In MState's study, 24% of respondents stopped using certain apps due to privacy concerns.
Today, an increasing number of people are actively protecting their data by refusing the services of companies and applications that use personal data. This is why Apple, Lyft, Dropbox, and Adobe have started taking a consumer-centric approach to data privacy. Consumers' understanding that their personal data is a commodity is increasing.
Free TON is a prime example of a secure blockchain. This blockchain has some of the best features compared to Ethereum, Binance Smart Chain, and Stellar. Data security directly depends on the use of blockchain. Each of the above blockchains provides a different level of data protection. Ethereum is the most popular blockchain, but Free TON may soon overtake it. This blockchain is just over a year old, and its capabilities exceed those of all other blockchains.
Many of us spend time in specialized telegram groups. The power over communication here belongs to random people with their own shortcomings. Conflict and abuse occurs regularly. Is there another way to keep order so that scam spam doesn't flourish and no one has total control over group members?
In my case, these thoughts led to the development and testing of a system that can be connected to your Telegram today.
And so here we find ourselves in the year of our lord 2021. Global crypto market capitalization is approaching $2 trillion. PayPal is launching a crypto checkout service. Lindsay Lohan is shilling Tron. The Dogecoin Super Bowl commercial didn’t happen, but Elon’s taking it “literally” to the moon instead. Our ascendancy is complete. Crypto is mainstream. But, even today, getting your hands on certain crypto assets can be a bit of an epic journey.
As we all are aware of the fact that the digital market is heavily leaning towards a reliable UX-driven process, app development has become quite complex, especially for targeting the industry for mobile platforms.
For every organization, creating a product that is beneficial for their customer needs always comes up with a plethora of challenges.
From the technical point of time, there are various challenges that every business faces, including selecting the right platform for the app, the right technology stack or framework, and creating an app that fulfills the needs and expectations of customers.
Similarly, there are more challenges that every business faces and needs to cope with while creating its dream product.
So, what to do??
Well, what if I say that the answer to all your queries and questions is Flutter app development with Artificial Intelligence (AI) integration……
Surprised? Wondering how?
Well, AI in Flutter app development is one of the best advancements in the software market. The concept of AI was first introduced during the 20th century with loads of innovations and advancements that we are still integrating into our mobile app development.
But, what are Artificial Intelligence and Flutter app development?
Hi All,
I’m pleased to invite you all to enroll in the Lviv Data Science Summer School, to delve into advanced methods and tools of Data Science and Machine Learning, including such domains as CV, NLP, Healthcare, Social Network Analysis, and Urban Data Science. The courses are practice-oriented and are geared towards undergraduates, Ph.D. students, and young professionals (intermediate level). The studies begin July 19–30 and will be hosted online. Make sure to apply — Spots are running fast!
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Dmitry Spodarets.
So we have already played with different neural networks. Cursed image generation using GANs, deep texts from GPT-2 — we have seen it all.
This time I wanted to create a neural entity that would act like a beauty blogger. This meant it would have to post pictures like Instagram influencers do and generate the same kind of narcissistic texts. \
Initially I planned to post the neural content on Instagram but using the Facebook Graph API which is needed to go beyond read-only was too painful for me. So I reverted to Telegram which is one of my favorite social products overall.
The name of the entity/channel (Aida Enelpi) is a bad neural-oriented pun mostly generated by the bot itself.
Hi All,
I have some good news for you…
Data Science Digest is back! We’ve been “offline” for a while, but no worries — You’ll receive regular digest updates with top news and resources on AI/ML/DS every Wednesday, starting today.
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And finally, your feedback is very much appreciated. Feel free to share any ideas with me and the team, and we’ll do our best to make Data Science Digest a better place for all.
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Dmitry Spodarets.
This article is a part of Algorithms in Go series where we discuss common algorithmic problems and their solution patterns.
In this edition, we take a closer look at bit manipulations. Bit operations can be extremely powerful and useful in an entire class of algorithmic problems, including problems that at first glance does not have to do anything with bits.
Let's consider the following problem: six friends meet in the bar and decide who pays for the next round. They would like to select a random person among them for that. How can they do a random selection using only a single coin?
The solution to this problem is not particularly obvious (for me:), so let's simplify a problem for a moment to develop our understanding. How would we do the selection if there were only three friends? In other words, how would we "mimic" a three-sided coin with a two-sided coin?
Author: Sergey Lukyanchikov, Sales Engineer at InterSystems
What is Distributed Artificial Intelligence (DAI)?
Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).
Distributed AI scenarios “for the masses”
We will not be discussing edge computations, confidential data operators, scattered mobile searches, or similar fascinating yet not the most consciously and wide-applied (not at this moment) scenarios. We will be much “closer to life” if, for instance, we consider the following scenario (its detailed demo can and should be watched here): a company runs a production-level AI/ML solution, the quality of its functioning is being systematically checked by an external data scientist (i.e., an expert that is not an employee of the company). For a number of reasons, the company cannot grant the data scientist access to the solution but it can send him a sample of records from a required table following a schedule or a particular event (for example, termination of a training session for one or several models by the solution). With that we assume, that the data scientist owns some version of the AI/ML mechanisms already integrated in the production-level solution that the company is running – and it is likely that they are being developed, improved, and adapted to concrete use cases of that concrete company, by the data scientist himself. Deployment of those mechanisms into the running solution, monitoring of their functioning, and other lifecycle aspects are being handled by a data engineer (the company employee).
