Java2Days 2023 Summer Edition will offer an exclusive opportunity for Java enthusiasts from around the world to come together and take a closer look at the latest trends in software development.

The summer edition of Java2Days 2023 promises to be a unique event of its kind, combining the warm and positive mood that summer brings and the excitement that lies behind technology.

Participants will be able to enjoy three days full of diverse activities – interesting talks, practical workshops and unique job opportunities. In addition, attendees will be able to meet and get to know new people with whom they will have the opportunity to build important relationships and learn many new things – in person, in the beautiful resort of Albena or online, from the comfort of home.

Here’s a glimpse of what’s in store for Java2Days 2023 Summer Edition:

🔸 Expert-led sessions: Learn from world-class speakers, including Java Champions and industry leaders, as they share their insights, experiences, and techniques to help you level up your Java game.

🔸 Hands-on workshops: Dive into practical, skill-building workshops designed to sharpen your expertise and expand your knowledge of the Java ecosystem.

🔸 Engaging panel discussions: Participate in thought-provoking conversations exploring the future of Java and its impact on the ever-evolving world of technology.

🔸 Networking opportunities: Connect with fellow Java enthusiasts, developers, and professionals from around the globe, both in person and virtually, to share ideas and forge lasting relationships.

🔸 Exclusive Swag: Grab your limited-edition Java2Days 2023 Summer Edition swag and merchandise to show off your Java pride!

Whether you decide to join the event in person at the breathtaking Albena resort, or join virtually, the summer edition of Java2Days 2023 is an event you won’t want to miss.

Mark your calendars and get ready to write the future of Java together, with sun, sea, and code!

For more information on Java2Days 2023 Summer Edition and to secure your tickets, visit www.java2days.com. Stay tuned for updates on speakers, sessions, and more!

“By expressing the functional as a neural network and incorporating these exact properties into the training data, we learn functionals free from important systematic errors — resulting in a better description of a broad class of chemical reactions.”

The team specifically addresses two long-standing problems with traditional functionals. That are the delocalization error and the spin symmetry breaking.

The company has used a neural network to represent the functional and tailoring its training dataset. The idea is to capture the fractional electron behavior expected for the exact functional. They have also found that they could solve the problems of delocalization and spin symmetry-breaking.

The function also showed itself to be accurate on broad, large-scale benchmarks. That is suggesting that this data-driven approach can capture aspects of the exact functional that have thus far been elusive, according to publication in DeepMind’s blog.

BofA Securities is acting as the sole underwriter for the offering.

The Company will not receive any of the proceeds from the sale of the shares being offered by the selling stockholder.

An automatic shelf registration statement on Form S-3 (including a prospectus) relating to these securities became effective upon filing with the Securities and Exchange Commission (the “SEC”). The Offering is being made solely by means of a prospectus supplement and the accompanying prospectus.

Bitcoin zigzags crazily on its way to a comeback

Over the 22 business days in July, Bitcoin swung a total of 40.7% from its low on July 20 of $29,308 to a high on July 31 of $41,598. By mid-morning on Aug. 2, it has fallen 5.6% from that summit to $39,396. On its two least volatile days, July 22 and 23, its variances from low to high were 2.8% and 2.9%. On 11 days, or half the time, it was worth at least 5% more at its best level than at its worst. Its biggest leap from bottom to top: 17.7% on July 26 on speculation that Amazon might be venturing into digital currencies.

While the instructors could share their knowledge with hundreds, even thousands, of students at a time during lectures, when it came to homework, large-scale and high-quality feedback on student assignments seemed like an insurmountable task. Chris Piech, assistant professor of computer science and co-creator of Code In Place said:

“It was a free class anyone in the world could take, and we got a whole bunch of humans to help us teach it. But the one thing we couldn’t really do is scale the feedback. We can scale instruction. We can scale content. But we couldn’t really scale feedback.”

To solve this problem, Piech worked with Chelsea Finn, assistant professor of computer science and of electrical engineering, and PhD students Mike Wu and Alan Cheng to develop and test a first-of-its-kind artificial intelligence teaching tool capable of assisting educators in grading and providing meaningful, constructive feedback for a high volume of student assignments.

Their innovative tool, which is detailed in a Stanford AI Lab blogpost, exceeded their expectations.

Teaching the AI tool

In education, it can be difficult to get lots of data for a single problem, like hundreds of instructor comments on one homework question. Companies that market online coding courses are often similarly limited, and therefore rely on multiple-choice questions or generic error messages when reviewing students’ work. Finn said:

“This task is really hard for machine learning because you don’t have a ton of data. Assignments are changing all the time, and they’re open-ended, so we can’t just apply standard machine learning techniques.”

The answer to scaling up feedback was a unique method called meta-learning, by which a machine learning system can learn about many different problems with relatively small amounts of data.

“With a traditional machine learning tool for feedback, if an exam changed, you’d have to retrain it, but for meta-learning, the goal is to be able to do it for unseen problems, so you can generalize it to new exams and assignments as well,” said Wu, who has studied computer science education for over three years.

The group found it much easier to get a little bit of data, like 20 pieces of feedback, on a large variety of problems. Using data from previous iterations of Stanford computer science courses, they were able to achieve accuracy at or above human level on 15,000 student submissions; a task not possible just one year earlier, the researchers remarked.