Dr. Uri Itai’s Post

Check out our new survey on reinforcement learning from human feedback (#RLHF) on arXiv! 🔗 https://lnkd.in/eitvmBh6 Timo Kaufmann Paul Weng Viktor Bengs Eyke Hüllermeier

This is the review of our Algorithms course. ❤️ We have covered each and every algorithm with a detailed analysis and proof of working, instead of asking to memorize algorithms without any intuition. For example - Why does Dijkstra work? Why does Bellman Ford work? What is the basic idea of Quick Sort? Proof of the master's theorem, etc. You can explore our free playlist on marathons of graph algorithms and recurrence relations here - https://lnkd.in/g9Hr3mkR

I had the honor of supporting Deborah Leipziger in the Launch of the Lexicon of Change https://lnkd.in/e5RnN2bE at the RISE World Summit last week. First, Karon Shaiva hosted Deborah and me for the formal Launch (video here https://lnkd.in/eqcjtfFR ), accompanied by an introductory exchange, where we touched on the transformative nature of language, and some of the words already integrated into the Lexicon from the r3.0 vocabulary, such as "Positive Maverick" https://lnkd.in/eq9uXRVi -- a term coined by Dr Raj T. in the context of finance, then generalized by us at r3.0. Then, Deborah and I explored deeper dimensions of the role of language in transformation in a Fireside Chat (video here https://lnkd.in/e8szyRTV ). In the aftermath, I realize so many words that need to make their way into the Lexicon, including incrementalism, phantom carrying capacity, etc... I wonder what you all think about the connection between language and the kinds of transformations we need to seed. Have you played a role in helping to birth new words? What words do we need to transform to be future fit? I look forward to hearing your perspectives!

GO Classes Algorithms Course Review. In-depth Course on Algorithms.. with Complete Analysis & NO Rote-Learning. #algorithms #goclasses

This is the review of our Algorithms course. ❤️ We have covered each and every algorithm with a detailed analysis and proof of working, instead of asking to memorize algorithms without any intuition. For example - Why does Dijkstra work? Why does Bellman Ford work? What is the basic idea of Quick Sort? Proof of the master's theorem, etc. You can explore our free playlist on marathons of graph algorithms and recurrence relations here - https://lnkd.in/g9Hr3mkR

Missed the workshop? No worries! Join us tomorrow for the final day. 🎉 🔍 𝐃𝐨 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐭𝐡𝐞 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞𝐬𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬? ❓ Why is smoothing important in IDF calculation? ❓ Why does the linear kernel of SVM perform well for text classification when using a TF-IDF vectorizer? These were some throwback interview questions! 🎯 If yes: comment else: 🚀 Join us on the final day of our workshop to learn the answers to these and more. Plus, we've got some surprises! Don't miss out! 🎉 📜 Get a certificate! 🏆

I'm excited to share that I have added 13 new articles and tutorials to the Circuit of Knowledge over the past four weeks! Here's an outline of the latest content: Optimization Methods and Overfitting: Seven tutorials exploring optimization techniques in machine learning and addressing overfitting. Maximum Likelihood Estimation and KL Divergence Minimization: Investigating the equivalence of these two statistical approaches. The Connection Between Cross Entropy and KL Divergence: Exploring the relationship between these fundamental concepts in information theory and machine learning. Information Theory Fundamentals: Introducing Shannon Entropy and Mutual Information, and their importance in machine learning and Data Science. Logistic Regression - Application to Bio-Assay by Joseph Berkson: A historical look at the logistic function's application in bio-assay experiments and its impact on modern techniques. Inverse Transform Sampling Method: Explaining how to generate random variables from a specified distribution, particularly for the maximum of uniform random variables. I hope you find these new additions valuable and enriching. Thank you for being a part of our learning community! #MachineLearning #DataScience #InformationTheory #Optimization #Statistics #LearningCommunity Read more on Circuit of Knowledge: https://lnkd.in/g9V4NMMn Please share so that this content can help other data scientists.

“𝘐 𝘩𝘢𝘷𝘦 𝘢 𝘵𝘳𝘶𝘭𝘺 𝘮𝘢𝘳𝘷𝘦𝘭𝘭𝘰𝘶𝘴 𝘥𝘦𝘮𝘰𝘯𝘴𝘵𝘳𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘵𝘩𝘪𝘴 𝘱𝘳𝘰𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯 𝘸𝘩𝘪𝘤𝘩 𝘵𝘩𝘪𝘴 𝘮𝘢𝘳𝘨𝘪𝘯 𝘪𝘴 𝘵𝘰𝘰 𝘯𝘢𝘳𝘳𝘰𝘸 𝘵𝘰 𝘤𝘰𝘯𝘵𝘢𝘪𝘯”. With these words, written in 𝟏𝟔𝟑𝟕, Pierre de Fermat intrigued and infuriated the scientific community. For well over 3 centuries, proving “𝘍𝘦𝘳𝘮𝘢𝘵'𝘴 𝘓𝘢𝘴𝘵 𝘛𝘩𝘦𝘰𝘳𝘦𝘮” had been the most notorious unsolved mathematical problem. A puzzle whose basics children could grasp but whose solution eluded the greatest mathematical minds. In 𝗝𝘂𝗻𝗲 𝟭𝟵𝟵𝟯, after years of hustling, Andrew Wiles announced, at the end of a lecture, that he had developed a proof. He could not predict the nightmare that would unfold. The academic world, it turned out, can be among the most ruthless, unforgiving, and sharp-elbowed environments you will ever experience. This is confirmed by my limited exposure (take my word for it, I have worked over 30 years on Wall Street 🤣). But it’d be a mistake to generalise, and I can say that academia is also home to some of the purest souls, and sharpest minds, you can fathom. Many I am fortunate to call friends. Unfortunately for Wiles, in 𝗔𝘂𝗴𝘂𝘀𝘁 𝟭𝟵𝟵𝟯, it was discovered that his proof contained a flaw. The sharks started to circle, and not just out of curiosity. Wiles tried and failed for over a year to repair his proof. The crucial idea for circumventing — rather than closing — the problematic area came to him in 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟭𝟵𝟵𝟰 (nearly exactly 30 years ago today) when he was on the verge of giving up. His successful revised proof (assisted by his colleague Richard Taylor) was published in 𝟭𝟵𝟵𝟱. Wiles' demonstration is more than 100 pages of complex mathematics, involving concepts such as Selmer groups, Hecke algebra, elliptic curves, modular forms, Euler systems and Galois representations. If you understand any of it 🙌🏼, please stop reading now and spare me further embarrassment. “𝘍𝘦𝘳𝘮𝘢𝘵'𝘴 𝘓𝘢𝘴𝘵 𝘛𝘩𝘦𝘰𝘳𝘦𝘮” by Simon Singh was published in 𝟭𝟵𝟵𝟳. I read it over 25 years ago. It is a fascinating tale of intellectual journeys — with the pace of a Netflix series. It is a testament to the grit, resilience, sacrifices and determination of Andrew Wiles who stood alone against all odds. In many ways it brings to the fore the perennial debate of when to (or not to) give up in the face of obstacles and widespread opposition. You may also think of Ibsen’s “𝘢𝘯 𝘦𝘯𝘦𝘮𝘺 𝘰𝘧 𝘵𝘩𝘦 𝘱𝘦𝘰𝘱𝘭𝘦”. It is also a question you will ponder, again and again, in business when faced with major strategic corporate finance or M&A decisions. Helpfully for you and me the maths there are more manageable. Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] “Nice post, Luigi” [ 𝗹𝗶𝗸𝗲 ] Want to see more [ 𝗳𝗼𝗹𝗹𝗼𝘄 ] #theoryinpracticeseries #TiPs #Leadership #financeeducation #mba #corporatefinance #mergersandacquisitions #columbiabusinessschool #imperialmeansbusiness

Let’s suppose that we have two types of data centers a and b which are already replicated 5 times. I want to build a new generation of 5 data centers. What is the architecture of the new type c of data centers that replicates the total capacity or ROI of the initial types b and c?

Diving into Correlations: From Definitions to ML Applications. Ever wondered what drives our ML models — correlations or causation? Let’s unravel the answers in my latest research! Explore the crucial insights inside. my course's task for ENG:Marwan Ahmed