#Data Engineering Classes

[avdgroupin]

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[1o1percentmilk]

grading papers for computer security class today and like 75% of the submissions are using chatgpt or something of the sort BECAUSE THE ANSWERS ARE ALL WRONG

[maximusblack-hp]

In my engineering class we have to do a survey depending on the topic each groups has This is a simple survey to help my group collect data on general on general questions based around Umbrella. Please answer a Survey

[topnotchquark]

Behind F1's Velvet Curtain

This article by Kate Wagner on her INEOS sponsored trip to the Austin GP at COTA last year was commissioned by Road and Track magazine and then taken down. Presumably because Kate has was pretty staunch in her opinions about what was essentially a paid trip.

It is exactly the kind of thing I have wanted to read about the felt experience of the money business of F1. It doesn't get into technicalities and does not produce any spreadsheets for reference. It's just, her experience of the presence of wealth in the sport.

She starts off by talking about how she has been covering cycling and NASCAR for a while now and both of those, in comparison, are scrappier sports with smaller sponsors and cheaper tickets.

What I also especially loved was how fascinated she was with the cars themselves, and how they seem like a true marvel of human engineering. She almost described the cars like these alien beasts that came into this dimension out of nowhere and were being constantly monitored and dueled with to furnish wins and glory (and shareholder value for sponsors).

I think I always had an understanding of the weird myth making surrounding F1 and the kind of media attention it attracts, but someone like Kate (who I have loved reading for a while now) putting it into perspective really made it click for me. This sport thrives off of the kind of cocoon it has built around it and understands exactly the certain exclusiveness it needs to maintain to keep the story alive.

Anyway, give it a read, especially because Road and Track is trying to bury it to not piss off sponsors.

[pleasantgentlementale]

At Fusion Software Institute, we offer dynamic courses blending theory and hands-on training to prepare you for a successful career in IT.

[jcmarchi]

Scientists use generative AI to answer complex questions in physics

New Post has been published on https://thedigitalinsider.com/scientists-use-generative-ai-to-answer-complex-questions-in-physics/

Scientists use generative AI to answer complex questions in physics

When water freezes, it transitions from a liquid phase to a solid phase, resulting in a drastic change in properties like density and volume. Phase transitions in water are so common most of us probably don’t even think about them, but phase transitions in novel materials or complex physical systems are an important area of study.

To fully understand these systems, scientists must be able to recognize phases and detect the transitions between. But how to quantify phase changes in an unknown system is often unclear, especially when data are scarce.

Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.

Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.

Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.

“If you have a new system with fully unknown properties, how would you choose which observable quantity to study? The hope, at least with data-driven tools, is that you could scan large new systems in an automated way, and it will point you to important changes in the system. This might be a tool in the pipeline of automated scientific discovery of new, exotic properties of phases,” says Frank Schäfer, a postdoc in the Julia Lab in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-author of a paper on this approach.

Joining Schäfer on the paper are first author Julian Arnold, a graduate student at the University of Basel; Alan Edelman, applied mathematics professor in the Department of Mathematics and leader of the Julia Lab; and senior author Christoph Bruder, professor in the Department of Physics at the University of Basel. The research is published today in Physical Review Letters.

Detecting phase transitions using AI

While water transitioning to ice might be among the most obvious examples of a phase change, more exotic phase changes, like when a material transitions from being a normal conductor to a superconductor, are of keen interest to scientists.

These transitions can be detected by identifying an “order parameter,” a quantity that is important and expected to change. For instance, water freezes and transitions to a solid phase (ice) when its temperature drops below 0 degrees Celsius. In this case, an appropriate order parameter could be defined in terms of the proportion of water molecules that are part of the crystalline lattice versus those that remain in a disordered state.

In the past, researchers have relied on physics expertise to build phase diagrams manually, drawing on theoretical understanding to know which order parameters are important. Not only is this tedious for complex systems, and perhaps impossible for unknown systems with new behaviors, but it also introduces human bias into the solution.

More recently, researchers have begun using machine learning to build discriminative classifiers that can solve this task by learning to classify a measurement statistic as coming from a particular phase of the physical system, the same way such models classify an image as a cat or dog.

The MIT researchers demonstrated how generative models can be used to solve this classification task much more efficiently, and in a physics-informed manner.

The Julia Programming Language, a popular language for scientific computing that is also used in MIT’s introductory linear algebra classes, offers many tools that make it invaluable for constructing such generative models, Schäfer adds.

Generative models, like those that underlie ChatGPT and Dall-E, typically work by estimating the probability distribution of some data, which they use to generate new data points that fit the distribution (such as new cat images that are similar to existing cat images).

However, when simulations of a physical system using tried-and-true scientific techniques are available, researchers get a model of its probability distribution for free. This distribution describes the measurement statistics of the physical system.

A more knowledgeable model

The MIT team’s insight is that this probability distribution also defines a generative model upon which a classifier can be constructed. They plug the generative model into standard statistical formulas to directly construct a classifier instead of learning it from samples, as was done with discriminative approaches.

“This is a really nice way of incorporating something you know about your physical system deep inside your machine-learning scheme. It goes far beyond just performing feature engineering on your data samples or simple inductive biases,” Schäfer says.

This generative classifier can determine what phase the system is in given some parameter, like temperature or pressure. And because the researchers directly approximate the probability distributions underlying measurements from the physical system, the classifier has system knowledge.

This enables their method to perform better than other machine-learning techniques. And because it can work automatically without the need for extensive training, their approach significantly enhances the computational efficiency of identifying phase transitions.

At the end of the day, similar to how one might ask ChatGPT to solve a math problem, the researchers can ask the generative classifier questions like “does this sample belong to phase I or phase II?” or “was this sample generated at high temperature or low temperature?”

Scientists could also use this approach to solve different binary classification tasks in physical systems, possibly to detect entanglement in quantum systems (Is the state entangled or not?) or determine whether theory A or B is best suited to solve a particular problem. They could also use this approach to better understand and improve large language models like ChatGPT by identifying how certain parameters should be tuned so the chatbot gives the best outputs.

In the future, the researchers also want to study theoretical guarantees regarding how many measurements they would need to effectively detect phase transitions and estimate the amount of computation that would require.

This work was funded, in part, by the Swiss National Science Foundation, the MIT-Switzerland Lockheed Martin Seed Fund, and MIT International Science and Technology Initiatives.

[moreaujeans]

YGHHHHFHF interviews are a joke

[intellbiinstitute]

Azure Data Engineering in PCMC: Career Path, Salary & Skills You Need in 2025

In today’s data-driven world, Azure Data Engineering has become one of the most in-demand career paths, especially for IT professionals looking to future-proof their careers. With companies relying heavily on cloud solutions and big data to drive decision-making, skilled Azure Data Engineers are at the heart of modern tech ecosystems.

If you're exploring opportunities in Azure Data Engineering in PCMC, now is the perfect time to upskill and tap into this booming field.

Career Path for an Azure Data Engineer

The career path usually starts with a background in IT, computer science, or data management. Entry-level roles like Data Analyst or SQL Developer can pave the way into Azure-specific roles. With hands-on experience in Azure tools, professionals can move into roles such as:

Azure Data Engineer

Cloud Data Architect

Data Platform Engineer

Big Data Engineer

This progression not only enhances technical skills but also opens doors to leadership and solution architect roles.

Salary Expectations in 2025

Salaries for Azure Data Engineers in India are increasing steadily. In PCMC and Pune, professionals can expect:

Entry-level: ₹5 – ₹8 LPA

Mid-level: ₹8 – ₹14 LPA

Senior-level: ₹15 LPA and above

With more organizations adopting Microsoft Azure for enterprise cloud solutions, the demand—and compensation—is only expected to grow in 2025.

Top Skills Required

To succeed in Azure Data Engineering, you'll need the following technical skills:

Microsoft Azure Data Factory

Azure SQL Database & Synapse Analytics

ETL and Data Pipelines

Power BI Integration

Big Data Tools like Spark, Hadoop

In addition to technical expertise, strong problem-solving and data modeling skills are essential.

Conclusion

Whether you're a recent graduate or a working professional, now is a great time to explore Azure Data Engineering in PCMC. With a clear career path, high-paying job roles, and future-ready skills, Azure offers a golden opportunity in 2025’s evolving IT landscape.

[iron-niffler]

started back at my internship today and man i feel wildly unqualified for this

[avdgroupin]

Data Engineering Course in Aurangabad

Learn to analyze and interpret large data sets with data engineering course in Aurangabad. Enroll now and kickstart your journey in this high paying rewarding career!

[ace-baker-rae]

I love this stuff so much. It started way before lidar too, there are great maps of the Mississippi done in the 1940's of the stream bed over time.

Lidar-Derived Aerial Maps Reveal the Dramatic Meandering Changes in River Banks Over Millennia

[tccicomputercoaching]

Python for Data Science Best Training at TCCI Ahmedabad! Learn key skills with expert trainers & flexible learning. Enroll now!

[aspiretechsoft]

🔥Understanding About Data Engineering and why they’re so important? #dataengineer

The Importance of Data Engineering in Data-Driven Organizations

Data engineering is critical for organizations that rely on data for decision-making because it provides the foundation for managing and utilizing vast amounts of data.

Data engineering is essential for building the infrastructure that powers advanced analytics, machine learning, business intelligence, and real-time data processing. As data volumes and complexity continue to grow, the role of data engineers will become increasingly vital.

So, if you’re looking to boost your data Engineer career, this Data Engineer course in Pune is the perfect opportunity to learn and get certified in the most in-demand skills today.

🔥Get in touch: 📞Call us: +91 – 7620830992

🌐 Enquire Now: Data Engineer course in Pune

[jvminstitute]

Data engineering courses in Pune

Are you searching top Data Engineering courses in Pune to enhance your skills in data management, big data technologies, and analytics. Learn from industry experts and build a strong foundation for a successful career in data engineering.

[orcelito]

Sometimes I wonder what my life would be like if I didn't switch out of engineering after my freshman year of college. I could've been a computer & electrical engineer.

Or if I'd pursued my middle school interest in architecture (that I still lowkey have). I used to draw floor plans just for the fun of it. I think it might've originated from building in the sims, bc I recently did a massive build in the sims 2 after years and years without playing, and I was having the time of my Life. I ended up deciding to pursue engineering in high school tho bc there's a family history to it (my grandpa was one, my sister is one, my dad studied it before dropping out of college, & my ex step grandpa was one too). Also it pays better lol.

But what if I didn't give it up? I could've been an architect. Just the other day I found out from European friends that their buildings don't tend to have ventilation systems built into the walls & I went on a whole nerd research binge learning about how European buildings have air circulation (it generally varies by region, colder climates often having ventilation systems while warmer climates often just get air circulation from windows). Yeah, the architecture interest is still there.

If I go Real far back, little me wanted to be a nurse lol. But that was just because my mom was one and I still looked up to her. I've long since accepted I wouldn't be able to make it as a nurse (I'm too squeamish + tend to get attached easily, so i think it'd be pretty soul crushing for me to work in a job where patients do die sometimes)

Idk. I'm close to finishing my degree in IT, so my general life path is pretty set. And it just has me wondering about the different jobs I've wanted throughout my life & what things would be like if I went to that instead.

[jcmarchi]

New AI noise-canceling headphone technology lets wearers pick which sounds they hear - Technology Org

New Post has been published on https://thedigitalinsider.com/new-ai-noise-canceling-headphone-technology-lets-wearers-pick-which-sounds-they-hear-technology-org/

New AI noise-canceling headphone technology lets wearers pick which sounds they hear - Technology Org

Most anyone who’s used noise-canceling headphones knows that hearing the right noise at the right time can be vital. Someone might want to erase car horns when working indoors but not when walking along busy streets. Yet people can’t choose what sounds their headphones cancel.

A team led by researchers at the University of Washington has developed deep-learning algorithms that let users pick which sounds filter through their headphones in real time. Pictured is co-author Malek Itani demonstrating the system. Image credit: University of Washington

Now, a team led by researchers at the University of Washington has developed deep-learning algorithms that let users pick which sounds filter through their headphones in real time. The team is calling the system “semantic hearing.” Headphones stream captured audio to a connected smartphone, which cancels all environmental sounds. Through voice commands or a smartphone app, headphone wearers can select which sounds they want to include from 20 classes, such as sirens, baby cries, speech, vacuum cleaners and bird chirps. Only the selected sounds will be played through the headphones.

The team presented its findings at UIST ’23 in San Francisco. In the future, the researchers plan to release a commercial version of the system.

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“Understanding what a bird sounds like and extracting it from all other sounds in an environment requires real-time intelligence that today’s noise canceling headphones haven’t achieved,” said senior author Shyam Gollakota, a UW professor in the Paul G. Allen School of Computer Science & Engineering. “The challenge is that the sounds headphone wearers hear need to sync with their visual senses. You can’t be hearing someone’s voice two seconds after they talk to you. This means the neural algorithms must process sounds in under a hundredth of a second.”

Because of this time crunch, the semantic hearing system must process sounds on a device such as a connected smartphone, instead of on more robust cloud servers. Additionally, because sounds from different directions arrive in people’s ears at different times, the system must preserve these delays and other spatial cues so people can still meaningfully perceive sounds in their environment.

Tested in environments such as offices, streets and parks, the system was able to extract sirens, bird chirps, alarms and other target sounds, while removing all other real-world noise. When 22 participants rated the system’s audio output for the target sound, they said that on average the quality improved compared to the original recording.

In some cases, the system struggled to distinguish between sounds that share many properties, such as vocal music and human speech. The researchers note that training the models on more real-world data might improve these outcomes.

Source: University of Washington

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