🔵 mesh. talks 🔵 - Machine learning stories from builders
​🔵 mesh. talks 🔵
​Want to hear inspiring stories about Machine Learning from passionate builders? 💥
​📣 Join us for Machine Learning Stories organized by mesh. This event brings together four amazing speakers who’ll share their latest research, projects and discoveries in machine learning today.
​It’s the perfect chance to connect with other curious minds, pick up new ideas, and get inspired about what’s next in ML. It doesn’t matter if you’re just interested or already in the field, we’re sure you’ll leave with great insights and new connections.
​This event is for you if:
​​You’re curious about tech innovations 🚀
​You’re interested in Machine Learning 🙋🏼
​You want to meet a group of builders and make new connections 🔗
​🕒 Event Agenda
​​16:40 - Check in 📝
​​17:00 - 1 Topic & 4 projects 🎤
​​19:00 - Q&A and Networking 💬
​​19:30 - Drinks together 🍻
​Csanád Budai
​Topic: Deep Reinforcement Learning and Its Applications within Robotics
​Description:
“The past few years have been extremely fruitful for Deep RL. Many exciting technologies, such as LLM fine-tuning and achievements like the Atari DQN model, have sparked interest in the possible applications of RL within robotics. After an introduction to the field, I will discuss the most exciting methods, open problems, and some of the things I'm working on at SZTAKI.”
​
​Béla Bálint
​Topic: Regression Model for Estimating Real Estate Prices in Hungary
​Description:
“We developed a regression model for estimating real estate prices in Hungary, integrating the econometric and socio-geographic characteristics of locations, as well as the exact geographical positioning. Additionally, we work with property descriptions, utilizing large language models to improve prediction accuracy. We also place significant emphasis on interpreting predictions through various techniques.”
​
​Petya Tallósy
​Topic: Generative Model That Won 2024 Nobel Prize in Physics: Boltzmann Machines
​Description:
Petya will dive into the cutting-edge generative model, Boltzmann machines, which recently earned a Nobel Prize in Physics. This talk will explore how these generative models work, from theory to application.
​
​Gergő Czehlár
​Topic: Optimization Techniques in Deep Learning: Beyond Gradient Descent
​Description:
Gergő’s session will cover innovative methods designed to push the limitations of gradient descent, giving us insights into more efficient, robust training methods for complex deep learning models.
​
​See you there 🙋🏼🙋🏻‍♀️
​team mesh.