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This lecture was a really simple & practical introduction to how machines learn Bayesian Learning, Bayes Theorem, Naive Bayes from Kimia Lab by Professor H.R.Tizhoosh. The lecture also walks through - MAP - Maximum Likelihood - Bayes Optimal Classifier - Naive Bayes model A really beginner-friendly lecture if you... show more

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰

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14,816 просмотров • 1 месяц назад •via X (Twitter)

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Introducing the 18th S R Nathan Fellow, Dr Shawn Lum, Senior Lecturer at The Asian School of the Environment, Nanyang Technological University. Through his work in research, teaching and conservation, Dr Lum has spent decades exploring the relationship between people and the natural world. Beyond the lecture hall and the field, he also has a lighter side — shaped by the experiences and stories that sparked his lifelong interest in nature. In his upcoming lecture series, “Singapore, Nature and a World Reimagined,” Dr Lum explores Singapore’s journey from the bold vision of a “Garden City” to the evolving ambition of becoming a “City in Nature.” The lectures examine how this transformation took shape over decades through careful planning, ecological understanding, and a shift in how society views nature. They also make a case for why biodiversity matters — not only for ecosystems, but for the well-being of people and communities, and how Singapore can better protect and value nature at home and beyond. 🎟️Sign up for his first lecture on 9 March 2026 here: 📺Watch his lecture series live on IPS’ Facebook page: 🌳Learn more about Dr Lum’s lectures:

Institute of Policy Studies - IPS

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There're few who can deliver both great AI research and charismatic talks. OpenAI Chief Scientist Ilya Sutskever is one of them. I watched Ilya's lecture at Simons Institute, where he delved into why unsupervised learning works through the lens of compression. Sharing my notes: - Kolmogorov compressor is the theoretical shortest-length program that produces a dataset. SGD is a practical approximation of the Kolmogorov search that finds an implicit program embedded in the weights of a soft computer, i.e. big Transformers. - Unsupervised learning is about computing the conditional Kolmogorov complexity of a target dataset given an unlabelled corpus, i.e. K(Y|X) - Theory tells us that optimizing for K(X, Y), the joint complexity, is as good as K(Y|X). So simply throw all data into the mix, and "just compress everything". - Joint compression is maximum likelihood over the giant concatenated dataset. - Ilya cites iGPT, Chen et al. 2020, to illustrate the ideas. iGPT is an image compressor that learns to predict the next pixel using a 1D sequence model. This is a phenomenal lecture, very accessible, and sometimes quite entertaining. YouTube: Lecture page:

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Learn to train an LLM with distributed data while ensuring privacy using federated learning in a new two-part short course, Intro to Federated Learning and Federated Fine-tuning of LLMs with Private Data, created with Flower and taught by Daniel J. Beutel and nic lane. Federated learning allows a single model to be trained across multiple devices, such as phones, or multiple organizations, such as hospitals, without the need to share data to a central server. This two-part course gives you an introduction to federated learning, and then teaches you how to fine-tune your large language model with distributed data using Flower Lab’s open source federated learning framework. You’ll learn: - How to use federated learning to train a variety of models, ranging from speech and vision models to LLMs, across distributed data while offering data privacy options to users and organizations. - Privacy Enhancing Technologies like differential privacy (DP), which obscures individual data by adding calibrated noise to query results. - Two variants of differential privacy - Central and Local - and how to choose depending on your use case. - How to measure and decrease bandwidth usage to make federated learning more practical and efficient with techniques like using pre-trained models and Parameter-Efficient Fine-Tuning - How federated LLM fine-tuning reduces the risk of leaking training data. Sign up here!

Andrew Ng

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Did a very different format with Reiner Pope – a blackboard lecture where he walks through how frontier LLMs are trained and served. It's shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk. It’s a bit technical, but I encourage you to hang in there - it’s really worth it. There are less than a handful of people who understand the full stack of AI, from chip design to model architecture, as well as Reiner. It was a real delight to learn from him. Recommend watching this one on YouTube so you can see the chalkboard. 0:00:00 – How batch size affects token cost and speed 0:31:59 – How MoE models are laid out across GPU racks 0:47:02 – How pipeline parallelism spreads model layers across racks 1:03:27 – Why Ilya said, “As we now know, pipelining is not wise.” 1:18:49 – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal 1:32:52 – Deducing long context memory costs from API pricing 2:03:52 – Convergent evolution between neural nets and cryptography

Did a very different format with Reiner Pope – a blackboard lecture where he walks through how frontier LLMs are trained and served. It's shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk. It’s a bit technical, but I encourage you to hang in there - it’s really worth it. There are less than a handful of people who understand the full stack of AI, from chip design to model architecture, as well as Reiner. It was a real delight to learn from him. Recommend watching this one on YouTube so you can see the chalkboard. 0:00:00 – How batch size affects token cost and speed 0:31:59 – How MoE models are laid out across GPU racks 0:47:02 – How pipeline parallelism spreads model layers across racks 1:03:27 – Why Ilya said, “As we now know, pipelining is not wise.” 1:18:49 – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal 1:32:52 – Deducing long context memory costs from API pricing 2:03:52 – Convergent evolution between neural nets and cryptography

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$Last semester a professor pulled me aside after class. "You trade prediction markets?" - he asked. I said yeah, mostly vibes. He laughed and said that's why you're losing. He spent 20 minutes on a whiteboard showing me Bayes' Theorem, Kelly Criterion, and LMSR pricing. Said this is literally how quant funds approach binary markets. I took photos of everything. Went home, opened Claude, pasted my notes and said: “build me a bot that does exactly this 3 hours later I had a working Bayesian trading bot for Polymarket BTC markets. Here's the math it runs on every single trade: - Bayesian update update beliefs before entering P(H|D) = P(D|H) · P(H) / P(D) - Edge detection -only trade if you have an edge EV = p̂ − p_market - Kelly sizing-bet exactly the right amount f* = EV / p̂(1−p̂) - LMSR pricing how Polymarket actually prices markets p(q) = e^{q/b} / Σe^{qⱼ/b} (it's literally the softmax function from neural networks) 65% win rate. Losses happen. Kelly keeps them from blowing the account. Compounding does the rest. My CLAWD BOT turned $100 into $1000 in 30min The professor was right. Vibes don't compound. Math does.$

Last semester a professor pulled me aside after class. "You trade prediction markets?" - he asked. I said yeah, mostly vibes. He laughed and said that's why you're losing. He spent 20 minutes on a whiteboard showing me Bayes' Theorem, Kelly Criterion, and LMSR pricing. Said this is literally how quant funds approach binary markets. I took photos of everything. Went home, opened Claude, pasted my notes and said: “build me a bot that does exactly this 3 hours later I had a working Bayesian trading bot for Polymarket BTC markets. Here's the math it runs on every single trade: - Bayesian update update beliefs before entering P(H|D) = P(D|H) · P(H) / P(D) - Edge detection -only trade if you have an edge EV = p̂ − p_market - Kelly sizing-bet exactly the right amount f* = EV / p̂(1−p̂) - LMSR pricing how Polymarket actually prices markets p(q) = e^{q/b} / Σe^{qⱼ/b} (it's literally the softmax function from neural networks) 65% win rate. Losses happen. Kelly keeps them from blowing the account. Compounding does the rest. My CLAWD BOT turned $100 into $1000 in 30min The professor was right. Vibes don't compound. Math does.

AdiiX

173,773 просмотров • 4 месяцев назад

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

124,417 просмотров • 12 часов назад

When noble prize winner Professor Robert Aumann talks about the genocide (what he calls "war") he says the story started with the mass expulsion. No, not the Nakba of Palestinians, but the Gaza settlers evacuated in 2005. It really isn't a very interesting clip, I must say, but it's amazing to me how someone supposedly representing the top of Zionist intellect have such a shallow and one-dimensional political view, not just a truly infantile perspective, but also factual ignorance (or just selective blindness to the facts). Really, they're so oblivious to reality, it's like Zionism completely distorts their ability to look around them and see what's going on. This is from a lecture that was published online 6 days ago, at the Jabotinski Institute. Link to the full lecture (not recommended) in the first comment.

When noble prize winner Professor Robert Aumann talks about the genocide (what he calls "war") he says the story started with the mass expulsion. No, not the Nakba of Palestinians, but the Gaza settlers evacuated in 2005. It really isn't a very interesting clip, I must say, but it's amazing to me how someone supposedly representing the top of Zionist intellect have such a shallow and one-dimensional political view, not just a truly infantile perspective, but also factual ignorance (or just selective blindness to the facts). Really, they're so oblivious to reality, it's like Zionism completely distorts their ability to look around them and see what's going on. This is from a lecture that was published online 6 days ago, at the Jabotinski Institute. Link to the full lecture (not recommended) in the first comment.

B.M.

28,726 просмотров • 1 год назад

Drake has always been strategic, throwback to how he was thinking when it came to picking a label. He discussed Roc-A-Fella Records or Def Jam, listen closely. New young artists can learn a lot here, but this also tells you nothing Drake accomplished is by luck, homie really had a locked in vision from day one.

Drake has always been strategic, throwback to how he was thinking when it came to picking a label. He discussed Roc-A-Fella Records or Def Jam, listen closely. New young artists can learn a lot here, but this also tells you nothing Drake accomplished is by luck, homie really had a locked in vision from day one.

Hip-Hop Unison

34,357 просмотров • 1 месяц назад

If you have time, watch the MIT lecture on Jacobian Matrices by Alan Edelman & Steven G. Johnson, & honestly, it may changes how you look at derivatives. Instead of treating derivatives as just formulas, they explain them as transformations of space. With proper rotation of images on live classes. haha Things they covered: - Difference between linear and non-linear transformations - How matrices act like operators that reshape geometry - Why the Jacobian is the best local linear approximation of a function - Understanding derivatives in higher dimensions - Intuition behind chain rule and automatic differentiation - Early foundations of backpropagation in ML One of the best parts was seeing how tiny input changes propagate through transformations mathematically. A really solid lecture if you want to build deeper intuition behind AI, optimization, and neural networks beyond just coding models. Full video link in comment.

If you have time, watch the MIT lecture on Jacobian Matrices by Alan Edelman & Steven G. Johnson, & honestly, it may changes how you look at derivatives. Instead of treating derivatives as just formulas, they explain them as transformations of space. With proper rotation of images on live classes. haha Things they covered: - Difference between linear and non-linear transformations - How matrices act like operators that reshape geometry - Why the Jacobian is the best local linear approximation of a function - Understanding derivatives in higher dimensions - Intuition behind chain rule and automatic differentiation - Early foundations of backpropagation in ML One of the best parts was seeing how tiny input changes propagate through transformations mathematically. A really solid lecture if you want to build deeper intuition behind AI, optimization, and neural networks beyond just coding models. Full video link in comment.

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰

60,360 просмотров • 2 месяцев назад

KJP is now under attack from the people for whom she relentlessly lied — as she attempts to gaslight and grift her way out of her own complicity. This exchange with Doocy about the Biden decline coverup is a stark reminder of how complicit she really was: “There's been no cover up. I want to be very clear about that." Now she’s writing a book to lecture Americans on disinformation and truth. Unreal.

KJP is now under attack from the people for whom she relentlessly lied — as she attempts to gaslight and grift her way out of her own complicity. This exchange with Doocy about the Biden decline coverup is a stark reminder of how complicit she really was: “There's been no cover up. I want to be very clear about that." Now she’s writing a book to lecture Americans on disinformation and truth. Unreal.

Western Lensman

64,673 просмотров • 1 год назад

Have you used quantization with an open source machine learning library, and wondered how quantization works? How can you preserve model accuracy as you compress from 32 bits to 16, 8, or even 2 bits? In our new short course, Quantization in Depth, taught by Hugging Face's Marc Sun and @younesbelkada, you'll learn to implement variants of linear quantization, such as asymmetric and symmetric modes, from scratch. You'll also quantize at different granularities (per-tensor, per-channel, per-group) to maintain performance. You’ll then construct a quantizer to compress any open source deep learning model’s dense layers to 8-bit precision. Finally, you’ll practice quantizing weights into 2 bits by packing four 2-bit weights into a single 8-bit integer. If you've ever run a large open source model on your laptop, you've likely benefited from someone's work in quantization. Come learn how this key technique works under the hood! Please sign up here:

Have you used quantization with an open source machine learning library, and wondered how quantization works? How can you preserve model accuracy as you compress from 32 bits to 16, 8, or even 2 bits? In our new short course, Quantization in Depth, taught by Hugging Face's Marc Sun and @younesbelkada, you'll learn to implement variants of linear quantization, such as asymmetric and symmetric modes, from scratch. You'll also quantize at different granularities (per-tensor, per-channel, per-group) to maintain performance. You’ll then construct a quantizer to compress any open source deep learning model’s dense layers to 8-bit precision. Finally, you’ll practice quantizing weights into 2 bits by packing four 2-bit weights into a single 8-bit integer. If you've ever run a large open source model on your laptop, you've likely benefited from someone's work in quantization. Come learn how this key technique works under the hood! Please sign up here:

Andrew Ng

198,628 просмотров • 2 лет назад