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These are 8 of the most important supervised machine learning algorithms you should know. 🧠 1️⃣ Linear Regression: Predicts a continuous value by modeling a linear relationship between inputs and the target. 2️⃣ Logistic Regression: Predicts the probability of a class, making it a common choice for classification. 3️⃣...

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Most people hear names like SVM, Random Forest, KNN, and XGBoost and never learn what they actually do. Professor Whitaker breaks down 8 of the most important supervised machine learning algorithms—how they learn from labeled data, how they make predictions, and why you’d choose one over another. From Linear Regression → XGBoost, this is the supervised ML map in one video. Save this for the next time machine learning starts sounding like alphabet soup. #MachineLearning #AI #ArtificialIntelligence #DataScience #ComputerScience
5:54

Most people hear names like SVM, Random Forest, KNN, and XGBoost and never learn what they actually do. Professor Whitaker breaks down 8 of the most important supervised machine learning algorithms—how they learn from labeled data, how they make predictions, and why you’d choose one over another. From Linear Regression → XGBoost, this is the supervised ML map in one video. Save this for the next time machine learning starts sounding like alphabet soup. #MachineLearning #AI #ArtificialIntelligence #DataScience #ComputerScience

FutureWithAI

48,900 views • 1 month ago

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):
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if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

109,764 views • 1 year ago

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):
0:52

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

117,748 views • 1 year ago

SVM by hand ✍️ ~ 19 steps walkthrough below (Linear vs RBF) Support Vector Machines reigned supreme in machine learning before the deep learning revolution. An SVM predicts with dot products, the same matrix multiplication every model uses. What it does not do is train by backpropagation: it is fitted by convex optimization, so there is no matrix-multiplication backward pass for a GPU to accelerate. I drew and calculated two SVMs by hand: a linear one (top) and an RBF one (bottom), classifying the same two test vectors. Goal: turn six training vectors and their learned coefficients into a prediction, and see what changing the kernel actually changes. = 1. Given = Six training vectors, their labels, and the coefficients and bias already learned. A coefficient of zero means that vector is not a support vector: too far from the boundary to matter. = 2. Linear kernel, test vector 1 = Let us take the dot product of the test vector with every training vector. The dot product stands in for cosine similarity, and the column of results is the first column of the kernel matrix K. = 3. Linear kernel, test vector 2 = We do the same for the second, and K is complete. = 4. Signed weights = Let us multiply each coefficient by its label. The second training vector drops out here, because its coefficient is 0. = 5. Weighted combination = We multiply the signed weights through K and add the bias b. The result is a signed distance to the decision boundary: 17 and 5. = 6. Classify = Let us take the sign. Both are positive. = 7 to 11. RBF kernel, test vector 1 = Now the same picture with a different kernel, in five moves: square the differences, sum them, take the square root for the L2 distance, multiply by minus gamma, and raise e to that power. The negation is what turns a distance into a similarity, and gamma controls how far a single training vector's influence reaches. = 12 to 16. RBF kernel, test vector 2 = We repeat all five. The numbers change, the moves do not. = 17 to 19. Decision boundary, again = Signed weights, weighted combination, sign. Identical arithmetic to steps 4 through 6, on a K that was built a completely different way. The outputs: Linear K, first column = [13, 25, 12, 15, 19, 27] Linear decision values = 17 and 5, both positive RBF decision values = -2 and 1, so negative and positive The takeaway: the kernel is the only thing that changed, and it changed the answer. The linear SVM calls both test vectors positive; the RBF one splits them. Everything after the kernel matrix, the signed weights and the weighted combination and the sign, is the same page of arithmetic twice. 💾 Save this post!
0:44

SVM by hand ✍️ ~ 19 steps walkthrough below (Linear vs RBF) Support Vector Machines reigned supreme in machine learning before the deep learning revolution. An SVM predicts with dot products, the same matrix multiplication every model uses. What it does not do is train by backpropagation: it is fitted by convex optimization, so there is no matrix-multiplication backward pass for a GPU to accelerate. I drew and calculated two SVMs by hand: a linear one (top) and an RBF one (bottom), classifying the same two test vectors. Goal: turn six training vectors and their learned coefficients into a prediction, and see what changing the kernel actually changes. = 1. Given = Six training vectors, their labels, and the coefficients and bias already learned. A coefficient of zero means that vector is not a support vector: too far from the boundary to matter. = 2. Linear kernel, test vector 1 = Let us take the dot product of the test vector with every training vector. The dot product stands in for cosine similarity, and the column of results is the first column of the kernel matrix K. = 3. Linear kernel, test vector 2 = We do the same for the second, and K is complete. = 4. Signed weights = Let us multiply each coefficient by its label. The second training vector drops out here, because its coefficient is 0. = 5. Weighted combination = We multiply the signed weights through K and add the bias b. The result is a signed distance to the decision boundary: 17 and 5. = 6. Classify = Let us take the sign. Both are positive. = 7 to 11. RBF kernel, test vector 1 = Now the same picture with a different kernel, in five moves: square the differences, sum them, take the square root for the L2 distance, multiply by minus gamma, and raise e to that power. The negation is what turns a distance into a similarity, and gamma controls how far a single training vector's influence reaches. = 12 to 16. RBF kernel, test vector 2 = We repeat all five. The numbers change, the moves do not. = 17 to 19. Decision boundary, again = Signed weights, weighted combination, sign. Identical arithmetic to steps 4 through 6, on a K that was built a completely different way. The outputs: Linear K, first column = [13, 25, 12, 15, 19, 27] Linear decision values = 17 and 5, both positive RBF decision values = -2 and 1, so negative and positive The takeaway: the kernel is the only thing that changed, and it changed the answer. The linear SVM calls both test vectors positive; the RBF one splits them. Everything after the kernel matrix, the signed weights and the weighted combination and the sign, is the same page of arithmetic twice. 💾 Save this post!

Tom Yeh

16,916 views • 3 months ago

25 algorithms every programmer should know: Let's start with my top favorite 10. If nothing else, you should read about these algorithms and have a good idea of how they work: 1. Linear search to find an element in a list 2. Binary search to find an element on a sorted list 3. Bubble sort to sort a list 4. Merge sort will also sort lists 5. Quicksort to sort the list and do it fast 6. Dijkstra to find the shortest path in a graph 7. Breadth-first Search (BFS) for trees or graphs 8. Depth-first search (DFS) for trees or graphs 9. Huffman for doing data compression 10. Anything related to dynamic programming Learning about algorithms is like getting tattoos: you never have enough. Here are another 5 algorithms that will help you go beyond the basics: 11. Kruskal for the finding minimum spanning tree 12. Floyd Warshall, shortest paths in a graph 13. Union Find to detect cycles in a graph 14. Bellman-Ford, shortest path in a graph 15. Lee for finding the shortest path in a maze If you are serious about this topic, I recommend learning about algorithms' space and time complexity. People usually refer to this topic as "Big O" notation. You should build a good intuition about the performance of different algorithms and learn how to evaluate them. Machine Learning will rule the next 50 years, so the next 10 algorithms you can't ignore are the following: 16. Linear Regression 17. Logistic Regression 18. Decision Trees 19. Bayes' theorem 20. k-Nearest Neighbors (kNN) 21. Every algorithm related to neural networks 22. K-means 23. Random forest 24. Gradient boosting algorithms 25. Any dimensionality reduction algorithm (PCA, for instance) There are many more mind-blowing algorithms! I haven't found a better way to understand how computers work from a first-principles point of view than reading about different algorithms. Take a look at the attached video.
2:53

25 algorithms every programmer should know: Let's start with my top favorite 10. If nothing else, you should read about these algorithms and have a good idea of how they work: 1. Linear search to find an element in a list 2. Binary search to find an element on a sorted list 3. Bubble sort to sort a list 4. Merge sort will also sort lists 5. Quicksort to sort the list and do it fast 6. Dijkstra to find the shortest path in a graph 7. Breadth-first Search (BFS) for trees or graphs 8. Depth-first search (DFS) for trees or graphs 9. Huffman for doing data compression 10. Anything related to dynamic programming Learning about algorithms is like getting tattoos: you never have enough. Here are another 5 algorithms that will help you go beyond the basics: 11. Kruskal for the finding minimum spanning tree 12. Floyd Warshall, shortest paths in a graph 13. Union Find to detect cycles in a graph 14. Bellman-Ford, shortest path in a graph 15. Lee for finding the shortest path in a maze If you are serious about this topic, I recommend learning about algorithms' space and time complexity. People usually refer to this topic as "Big O" notation. You should build a good intuition about the performance of different algorithms and learn how to evaluate them. Machine Learning will rule the next 50 years, so the next 10 algorithms you can't ignore are the following: 16. Linear Regression 17. Logistic Regression 18. Decision Trees 19. Bayes' theorem 20. k-Nearest Neighbors (kNN) 21. Every algorithm related to neural networks 22. K-means 23. Random forest 24. Gradient boosting algorithms 25. Any dimensionality reduction algorithm (PCA, for instance) There are many more mind-blowing algorithms! I haven't found a better way to understand how computers work from a first-principles point of view than reading about different algorithms. Take a look at the attached video.

Santiago

274,344 views • 2 years ago

Today we introduced a novel system one model, Clairic, based on regression instead of classification (i.e. decision models like Clef). This enables estimation of continuous values without having to exhaustively list choices upfront. It also enables very efficient extraction of structured data from unstructured inputs. Open weights coming soon!
1:22

Today we introduced a novel system one model, Clairic, based on regression instead of classification (i.e. decision models like Clef). This enables estimation of continuous values without having to exhaustively list choices upfront. It also enables very efficient extraction of structured data from unstructured inputs. Open weights coming soon!

Kevin Flansburg

129,483 views • 25 days ago

➡️ Machine Learning from Scratch, part 40: is linear regression machine learning? Linear regression is machine learning, because it learns a weight and a bias from examples with known answers, which makes it supervised learning in its simplest form. What makes it special is that the best weight and bias can also be computed in one step with the normal equation, w = (X^T X)^-1 X^T y, so standard tools usually solve it directly with a least squares routine without any training loop. Gradient descent comes back when the data gets really huge, and it trains neural networks, where there is generally no such formula. We ran everything ourselves and downloaded nothing. linreg_ml.py (14 lines of numpy) takes the same eight points as part 1. Gradient descent with the same settings as part 1 (start at 0, learning rate 0.01) still has a bias of 1.995 after 1,000 steps, while the formula gives 2.029 (weight 0.796), and it needs 2,052 steps to come within 0.0005 of the formula. The least squares routine gives the same numbers as the formula. Honest notes: on eight points the formula is instant, and on millions of rows with many features the picture changes, which is when step by step training pays off. #machinelearning #ai #linearregression #datascience #coding
1:41

➡️ Machine Learning from Scratch, part 40: is linear regression machine learning? Linear regression is machine learning, because it learns a weight and a bias from examples with known answers, which makes it supervised learning in its simplest form. What makes it special is that the best weight and bias can also be computed in one step with the normal equation, w = (X^T X)^-1 X^T y, so standard tools usually solve it directly with a least squares routine without any training loop. Gradient descent comes back when the data gets really huge, and it trains neural networks, where there is generally no such formula. We ran everything ourselves and downloaded nothing. linreg_ml.py (14 lines of numpy) takes the same eight points as part 1. Gradient descent with the same settings as part 1 (start at 0, learning rate 0.01) still has a bias of 1.995 after 1,000 steps, while the formula gives 2.029 (weight 0.796), and it needs 2,052 steps to come within 0.0005 of the formula. The least squares routine gives the same numbers as the formula. Honest notes: on eight points the formula is instant, and on millions of rows with many features the picture changes, which is when step by step training pays off. #machinelearning #ai #linearregression #datascience #coding

MachineLearningToGo

22,800 views • 24 days ago

➡️ Quantitative Finance From Scratch, Part 21: Linear Regression in finance Linear regression finds the straight line that best describes how one quantity moves with another. In finance, that is often a stock's daily return against the market's daily return. Least squares: for every day, you measure how far the real return lies from the line, square that distance and add everything up. The best line makes this sum as small as possible. The line has two numbers. The slope is the beta from Part 14: how strongly the stock follows the market. The intercept is alpha: what the stock earns on average on a day when the market does not move. What the line cannot explain is the residual, and R² tells you which share of the swings (the variance) the line explains, from 0 for nothing to 1 for everything. In this video we simulate two years (504 trading days) of a market and a stock with a built-in beta of 1.2, a second factor with a beta of 0.6 and no alpha. The line finds a beta of 1.26 (the 1.2 lies within the range of chance for two years of data), an alpha of +0.05% a day and an R² of 52%. With two inputs, the line becomes a plane: betas 1.27 and 0.60, R² 63%. Quants use regression to estimate betas, to size hedges, to test whether a strategy earns more than its risk, and to check whether two assets really move together. The limits: a regression only finds a relationship in the data you give it. It does not prove that one thing causes the other, a few extreme days can pull the line (three invented extreme days move our slope from 1.26 to 1.10), and a relationship from the past can change. Education only, not financial advice. #quantitativefinance #quant #finance #regression #beta
1:45

➡️ Quantitative Finance From Scratch, Part 21: Linear Regression in finance Linear regression finds the straight line that best describes how one quantity moves with another. In finance, that is often a stock's daily return against the market's daily return. Least squares: for every day, you measure how far the real return lies from the line, square that distance and add everything up. The best line makes this sum as small as possible. The line has two numbers. The slope is the beta from Part 14: how strongly the stock follows the market. The intercept is alpha: what the stock earns on average on a day when the market does not move. What the line cannot explain is the residual, and R² tells you which share of the swings (the variance) the line explains, from 0 for nothing to 1 for everything. In this video we simulate two years (504 trading days) of a market and a stock with a built-in beta of 1.2, a second factor with a beta of 0.6 and no alpha. The line finds a beta of 1.26 (the 1.2 lies within the range of chance for two years of data), an alpha of +0.05% a day and an R² of 52%. With two inputs, the line becomes a plane: betas 1.27 and 0.60, R² 63%. Quants use regression to estimate betas, to size hedges, to test whether a strategy earns more than its risk, and to check whether two assets really move together. The limits: a regression only finds a relationship in the data you give it. It does not prove that one thing causes the other, a few extreme days can pull the line (three invented extreme days move our slope from 1.26 to 1.10), and a relationship from the past can change. Education only, not financial advice. #quantitativefinance #quant #finance #regression #beta

QuantFinanceToGo

59,100 views • 1 month ago

A 91-year-old professor is why Nvidia is worth $4 trillion. His name is Gilbert Strang. He teaches linear algebra at MIT. Every AI model on Earth runs on his course. The course has been free on YouTube since 2005. The videos have earned him nothing. MIT 18.06 opens with "The Geometry of Linear Equations." No advanced math. Strang takes a system of two equations, draws it two ways, and shows the class that a matrix is a picture, not an abstraction. The row picture is two lines that cross. The column picture is two arrows that sum to a target. Every neural network on Earth operates on the column picture. Strang first taught linear algebra at MIT in 1962. He wrote the textbook in 1976. It is on every serious engineer's shelf. Every quant fund, every ML lab, every rendering engine at Pixar is running his math. His central insight is that most people are taught matrices as bookkeeping. That is the first thing to unlearn. A matrix is a linear transformation. A linear transformation is a way of moving space. Once you see the space move, the math stops being algebra and becomes geometry. The Kalman filter is a linear system. PCA is a linear system. Every gradient step in a neural net is a matrix-vector product. GPT is a stack of matrix-vector products, each one a scene from MIT 18.06 running on a Blackwell GPU. He retired in 2023 after 61 years at MIT. The course is still up. Watched tens of millions of times. The chip is $40,000. Strang never asked for a royalty.
37:24

A 91-year-old professor is why Nvidia is worth $4 trillion. His name is Gilbert Strang. He teaches linear algebra at MIT. Every AI model on Earth runs on his course. The course has been free on YouTube since 2005. The videos have earned him nothing. MIT 18.06 opens with "The Geometry of Linear Equations." No advanced math. Strang takes a system of two equations, draws it two ways, and shows the class that a matrix is a picture, not an abstraction. The row picture is two lines that cross. The column picture is two arrows that sum to a target. Every neural network on Earth operates on the column picture. Strang first taught linear algebra at MIT in 1962. He wrote the textbook in 1976. It is on every serious engineer's shelf. Every quant fund, every ML lab, every rendering engine at Pixar is running his math. His central insight is that most people are taught matrices as bookkeeping. That is the first thing to unlearn. A matrix is a linear transformation. A linear transformation is a way of moving space. Once you see the space move, the math stops being algebra and becomes geometry. The Kalman filter is a linear system. PCA is a linear system. Every gradient step in a neural net is a matrix-vector product. GPT is a stack of matrix-vector products, each one a scene from MIT 18.06 running on a Blackwell GPU. He retired in 2023 after 61 years at MIT. The course is still up. Watched tens of millions of times. The chip is $40,000. Strang never asked for a royalty.

Ochob

131,950 views • 3 months ago

Introducing Architect I am releasing the first iteration of a model which generates optimized project plans with a single prompt. Think Linear or JIRA, but for agents. A living model that learns from the gossiping network, and gets smarter with every interaction.
0:25

Introducing Architect I am releasing the first iteration of a model which generates optimized project plans with a single prompt. Think Linear or JIRA, but for agents. A living model that learns from the gossiping network, and gets smarter with every interaction.

Varun

56,906 views • 7 months ago

Linear Agent × Code Intelligence Ask Linear how a feature is implemented, why something behaves a certain way, or which technical constraints should shape a project plan – without digging through the codebase or interrupting an engineer.
0:33

Linear Agent × Code Intelligence Ask Linear how a feature is implemented, why something behaves a certain way, or which technical constraints should shape a project plan – without digging through the codebase or interrupting an engineer.

Linear

18,715 views • 6 months ago

Data often lie on a low-dimensional manifold embedded in a high-dimensional space. But these manifolds are often highly non-linear, making linear dimensionality reduction methods like PCA insufficient. This has motivated the development of non-linear dimensionality reduction.
0:21

Data often lie on a low-dimensional manifold embedded in a high-dimensional space. But these manifolds are often highly non-linear, making linear dimensionality reduction methods like PCA insufficient. This has motivated the development of non-linear dimensionality reduction.

Alec Helbling

212,854 views • 1 year ago

I genuinely thought “linear algebra” meant stuff like 2x = y. Then you open the class and suddenly you’re multiplying giant boxes of numbers. The whole subject gets way easier to understand once you see the core idea: a matrix can act like a rule, a vector can describe a location, and the rule can move that location. That’s why linear algebra shows up everywhere from computer graphics to engineering to machine learning. #LearnOnTikTokContest
1:36

I genuinely thought “linear algebra” meant stuff like 2x = y. Then you open the class and suddenly you’re multiplying giant boxes of numbers. The whole subject gets way easier to understand once you see the core idea: a matrix can act like a rule, a vector can describe a location, and the rule can move that location. That’s why linear algebra shows up everywhere from computer graphics to engineering to machine learning. #LearnOnTikTokContest

Gravity Assist

340,200 views • 1 month ago

Announcing the newest releases from Meta FAIR. We’re releasing new groundbreaking models, benchmarks, and datasets that will transform the way researchers approach molecular property prediction, language processing, and neuroscience. 1️⃣ Open Molecules 2025 (OMol25): A dataset for molecular discovery with simulations of large atomic systems. 2️⃣ Universal Model for Atoms: A machine learning interatomic potential for modeling atom interactions across a wide range of materials and molecules. 3️⃣ Adjoint Sampling: A scalable algorithm for training generative models based on scalar rewards. 4️⃣ FAIR and the Rothschild Foundation Hospital partnered on a large-scale study that reveals striking parallels between language development in humans and LLMs. Read more ➡️
1:53

Announcing the newest releases from Meta FAIR. We’re releasing new groundbreaking models, benchmarks, and datasets that will transform the way researchers approach molecular property prediction, language processing, and neuroscience. 1️⃣ Open Molecules 2025 (OMol25): A dataset for molecular discovery with simulations of large atomic systems. 2️⃣ Universal Model for Atoms: A machine learning interatomic potential for modeling atom interactions across a wide range of materials and molecules. 3️⃣ Adjoint Sampling: A scalable algorithm for training generative models based on scalar rewards. 4️⃣ FAIR and the Rothschild Foundation Hospital partnered on a large-scale study that reveals striking parallels between language development in humans and LLMs. Read more ➡️

AI at Meta

900,014 views • 1 year ago

Ever wondered what neural networks are and how they work? Systems like ChatGPT use neural networks to work as well as they do. Neural networks are composed of "layers" of neurons, layers with different functions; connections between layers called "weights"; and mathematical functions called "activation functions". If you’re interested in learning about these systems, check the comments. Ultimately, the neural network structure of the model serves to visually demonstrate that it is, in fact, a complex mathematical equation. When companies release the model's weights, they are releasing a key component needed to run the model's complete equation. Without the weights, the equation is incomplete. For the math-minded: the weights of a model are the learned numbers (they are variables during training) that are then used as constants in the mathematical functions that make up the model. Neural networks are ultimately just one big, hyper-complex mathematical function, and when a model is trained, it learns the constants associated with the high-dimensional input.
2:37

Ever wondered what neural networks are and how they work? Systems like ChatGPT use neural networks to work as well as they do. Neural networks are composed of "layers" of neurons, layers with different functions; connections between layers called "weights"; and mathematical functions called "activation functions". If you’re interested in learning about these systems, check the comments. Ultimately, the neural network structure of the model serves to visually demonstrate that it is, in fact, a complex mathematical equation. When companies release the model's weights, they are releasing a key component needed to run the model's complete equation. Without the weights, the equation is incomplete. For the math-minded: the weights of a model are the learned numbers (they are variables during training) that are then used as constants in the mathematical functions that make up the model. Neural networks are ultimately just one big, hyper-complex mathematical function, and when a model is trained, it learns the constants associated with the high-dimensional input.

Harper Carroll

50,905 views • 1 year ago

➡️ Machine Learning from Scratch, part 48: the Boltzmann machine. In 1985 Hinton, David Ackley and Terrence Sejnowski published how a Boltzmann machine learns, starting from the Hopfield network of part 47. Like the Hopfield network it prefers pictures with low energy, but it also has hidden units next to the visible pixels, and its units switch on by chance: the sigmoid turns the weighted sum into a probability. So the machine never settles and visits low energy pictures more often. We use the restricted version, with connections only between the two layers. The code (boltzmann.py, 16 lines of numpy) trains a restricted Boltzmann machine with 9 visible and 8 hidden units on all 14 bars-and-stripes pictures of 3 x 3 pixels, with contrastive divergence (one dream step per round, 5,000 rounds). Then 1,000 dreams start from pure noise and switch back and forth 100 times: 855 of them became one of the 14 pictures, from 3 times for the empty picture to 142 times for one with two bars. Sources: Nobel Prize in Physics 2024, press release of 8 October 2024 (Hopfield and Hinton, "for foundational discoveries and inventions that enable machine learning with artificial neural networks"); D. H. Ackley, G. E. Hinton, T. J. Sejnowski, "A Learning Algorithm for Boltzmann Machines", Cognitive Science, 1985; G. E. Hinton, "Training Products of Experts by Minimizing Contrastive Divergence", Neural Computation, 2002. Honest notes: the pictures do not come up equally often, because one dream step per round is only a shortcut for the real learning rule. When two of the 14 pictures are left out of training, our small machine does not dream them up; new examples of the same kind come from large machines trained on many examples. #machinelearning #ai #boltzmannmachine #neuralnetworks #nobelprize
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➡️ Machine Learning from Scratch, part 48: the Boltzmann machine. In 1985 Hinton, David Ackley and Terrence Sejnowski published how a Boltzmann machine learns, starting from the Hopfield network of part 47. Like the Hopfield network it prefers pictures with low energy, but it also has hidden units next to the visible pixels, and its units switch on by chance: the sigmoid turns the weighted sum into a probability. So the machine never settles and visits low energy pictures more often. We use the restricted version, with connections only between the two layers. The code (boltzmann.py, 16 lines of numpy) trains a restricted Boltzmann machine with 9 visible and 8 hidden units on all 14 bars-and-stripes pictures of 3 x 3 pixels, with contrastive divergence (one dream step per round, 5,000 rounds). Then 1,000 dreams start from pure noise and switch back and forth 100 times: 855 of them became one of the 14 pictures, from 3 times for the empty picture to 142 times for one with two bars. Sources: Nobel Prize in Physics 2024, press release of 8 October 2024 (Hopfield and Hinton, "for foundational discoveries and inventions that enable machine learning with artificial neural networks"); D. H. Ackley, G. E. Hinton, T. J. Sejnowski, "A Learning Algorithm for Boltzmann Machines", Cognitive Science, 1985; G. E. Hinton, "Training Products of Experts by Minimizing Contrastive Divergence", Neural Computation, 2002. Honest notes: the pictures do not come up equally often, because one dream step per round is only a shortcut for the real learning rule. When two of the 14 pictures are left out of training, our small machine does not dream them up; new examples of the same kind come from large machines trained on many examples. #machinelearning #ai #boltzmannmachine #neuralnetworks #nobelprize

MachineLearningToGo

14,700 views • 13 days ago

The person you choose to partner with in life is the single most important decision you will ever make. The most important piece of advice I would share: Focus more on growing in love than falling in love. The path to a fulfilling relationship is found through focusing less on the falling and more on the growing.
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The person you choose to partner with in life is the single most important decision you will ever make. The most important piece of advice I would share: Focus more on growing in love than falling in love. The path to a fulfilling relationship is found through focusing less on the falling and more on the growing.

Sahil Bloom

444,993 views • 2 years ago

THE CEO OF OPENAI SAM ALTMAN JUST EXPLAINED WHY MOST PEOPLE THINK ABOUT DECISION-MAKING COMPLETELY WRONG There are only a handful of truly strategic decisions - maybe 1-2 a month, not a year - but that doesn't mean the rest don't matter. The difference between WHAT decisions and HOW decisions - and most people only think about the first kind. Are we building ChatGPT or not - that's one WHAT decision. But making that decision successful requires 10,000 small HOW decisions along the way - and that's exactly where companies win or lose. People who say a CEO makes 1-2 decisions a year have simply never tried to run a complex organization - because at the execution level every single day is dozens of HOW decisions that either reinforce or destroy that one strategic WHAT.
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THE CEO OF OPENAI SAM ALTMAN JUST EXPLAINED WHY MOST PEOPLE THINK ABOUT DECISION-MAKING COMPLETELY WRONG There are only a handful of truly strategic decisions - maybe 1-2 a month, not a year - but that doesn't mean the rest don't matter. The difference between WHAT decisions and HOW decisions - and most people only think about the first kind. Are we building ChatGPT or not - that's one WHAT decision. But making that decision successful requires 10,000 small HOW decisions along the way - and that's exactly where companies win or lose. People who say a CEO makes 1-2 decisions a year have simply never tried to run a complex organization - because at the execution level every single day is dozens of HOW decisions that either reinforce or destroy that one strategic WHAT.

Noisy

32,434 views • 5 months ago

What is a decision model? Jev by TypeSafe AI answers yes/no and multiple-choice questions, with a confidence score. Much of software development are a sequence of decisions, and Jev is 10x cheaper and faster than an LLM. Let’s understand this through practical examples:
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What is a decision model? Jev by TypeSafe AI answers yes/no and multiple-choice questions, with a confidence score. Much of software development are a sequence of decisions, and Jev is 10x cheaper and faster than an LLM. Let’s understand this through practical examples:

OpenRouter

169,880 views • 1 month ago

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG
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My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG

QuantHood

16,376 views • 4 months ago