NewsLetter 24
Greetings readers,
Hope you’re all doing well. As the freshers settle in, Turing Hut is preparing an epic lineup of events for this academic year, alongside welcoming another peer-to-peer learning batch. Stay tuned for what’s to come!
Opportunities
Highlights
Team Turing Hut, in collaboration with AIESEC in Hyderabad and FCC IIT Hyderabad, and powered by NxtWave, successfully conducted Round 2 of Hack4SDG. The ideathon received 160 team submissions in Round 1, and the top 30 teams were selected to pitch their solutions in front of a distinguished jury. Seven teams advanced to the final round, showcasing outstanding innovation in addressing the United Nations' Sustainable Development Goals. The event was a great success, filled with insightful discussions and promising ideas, paving the way for the upcoming final hackathon at IIT Hyderabad.
This is just the beginning! Our upcoming event in October will take us back to the thrill of competitive programming, promising even more excitement and skill development for all participants. Stay tuned!
Interesting Reads
- Brain Dynamics: Unraveling Decision-Making Processes
Neuroscientists at the Sainsbury Wellcome Centre have revealed how sensory input transforms into motor action across multiple brain regions in mice. This research indicates that decision-making is a coordinated global process influenced by learning, potentially informing artificial intelligence designs for distributed neural networks. Using advanced Neuropixels probes, the team recorded data from over 15,000 neurons, demonstrating that trained mice integrate sensory evidence across the entire brain, unlike naïve mice, who primarily use their visual system. The findings, published in Nature, highlight the complexity of how the brain processes perceptual decisions and the importance of learning in this dynamic.
- Hyperparameter Tuning: Mastering It with Optuna
In machine learning, selecting the right hyperparameters is crucial for maximizing model performance, as these settings determine how your model learns from data. This article delves into Optuna, an effective framework for hyperparameter optimization that enhances model accuracy and generalization. We will explore the underlying algorithms, including Bayesian Optimization and Tree-structured Parzen Estimator (TPE), and examine the mathematical principles that drive Optuna's functionality. With practical examples using XGBoost and neural networks in PyTorch, you’ll gain valuable insights into harnessing Optuna for your machine learning projects. Prepare for a deep dive into hyperparameter tuning!.
Todo Problem
Here's the link to a wonderful question that you should check out: problem link.
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Thanks to Akshaya, Aniketh and Shailesh for contributing to the newsletter