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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...

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Наука и технологии#machinelearning#ai#boltzmannmachine#neuralnetworks#nobelprize
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The 2024 Nobel Prize in Physics has been awarded to Geoffrey Hinton and John Hopfield for machine learning with artificial neural networks
0:28

The 2024 Nobel Prize in Physics has been awarded to Geoffrey Hinton and John Hopfield for machine learning with artificial neural networks

Tsarathustra

106,875 просмотров • 2 лет назад

➡️ Machine Learning from Scratch, part 47: the Hopfield network. In 1982 the physicist John Hopfield described a network that works as a memory, and 42 years later this work was part of the Nobel Prize in Physics. Each neuron is one pixel that is either on or off, and every neuron is connected to every other neuron, so 100 pixels need 4,950 connections. There are no layers: signals go back and forth until the network settles. Each neuron takes a weighted sum of its inputs and switches on if the sum is positive, off if it is negative. Storing a picture is simple: for every pair of pixels with the same colour the connection gets stronger, otherwise weaker. Every stored picture becomes a valley in the energy landscape, and no switch can ever raise the energy, so a noisy picture rolls downhill into the nearest stored memory. The code (hopfield.py, 16 lines of numpy, pictures in pics.txt) stores three 10 x 10 pictures, a heart, a house and a smiley, and flips 30 of the 100 pixels in each. After a single pass over all neurons all three come back exactly; the energy of the heart falls from -6.90 to -48.90, the energy of the stored heart. With 100 neurons and random patterns (10 flipped pixels, 20 runs) it recalls 5 patterns every time, 10 patterns in 94.5 % of the cases, 14 in 68 %, 20 in about one in four (24 %) and 30 in 1 %. Honest notes: the weights are kept as whole numbers (100 times the Hebb weight), so a sum of exactly zero switches the neuron on, as the rule says. The landscape in the video is a picture of the idea; the valley floors and the height of the rolling ball are the real energies from the run. #machinelearning #ai #coding #neuralnetworks #nobelprize
2:13

➡️ Machine Learning from Scratch, part 47: the Hopfield network. In 1982 the physicist John Hopfield described a network that works as a memory, and 42 years later this work was part of the Nobel Prize in Physics. Each neuron is one pixel that is either on or off, and every neuron is connected to every other neuron, so 100 pixels need 4,950 connections. There are no layers: signals go back and forth until the network settles. Each neuron takes a weighted sum of its inputs and switches on if the sum is positive, off if it is negative. Storing a picture is simple: for every pair of pixels with the same colour the connection gets stronger, otherwise weaker. Every stored picture becomes a valley in the energy landscape, and no switch can ever raise the energy, so a noisy picture rolls downhill into the nearest stored memory. The code (hopfield.py, 16 lines of numpy, pictures in pics.txt) stores three 10 x 10 pictures, a heart, a house and a smiley, and flips 30 of the 100 pixels in each. After a single pass over all neurons all three come back exactly; the energy of the heart falls from -6.90 to -48.90, the energy of the stored heart. With 100 neurons and random patterns (10 flipped pixels, 20 runs) it recalls 5 patterns every time, 10 patterns in 94.5 % of the cases, 14 in 68 %, 20 in about one in four (24 %) and 30 in 1 %. Honest notes: the weights are kept as whole numbers (100 times the Hebb weight), so a sum of exactly zero switches the neuron on, as the rule says. The landscape in the video is a picture of the idea; the valley floors and the height of the rolling ball are the real energies from the run. #machinelearning #ai #coding #neuralnetworks #nobelprize

MachineLearningToGo

12,000 просмотров • 14 дней назад

What do you use AI for? "The Godfather of AI", Geoffrey Hinton, shared what he likes using AI for during our Nobel Prize Conversations event in Madrid, Spain. Hinton was awarded the 2024 Nobel Prize in Physics “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” Watch the full event here:
0:29

What do you use AI for? "The Godfather of AI", Geoffrey Hinton, shared what he likes using AI for during our Nobel Prize Conversations event in Madrid, Spain. Hinton was awarded the 2024 Nobel Prize in Physics “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” Watch the full event here:

The Nobel Prize

28,598 просмотров • 10 месяцев назад

Yann LeCun says the recent Nobel Prizes related to AI were the result of the Nobel Committee feeling under pressure to recognize the impact of deep learning, and the Hopfield nets and Boltzmann machines for which John Hopfield and Geoff Hinton were honored are "completely useless"
2:03

Yann LeCun says the recent Nobel Prizes related to AI were the result of the Nobel Committee feeling under pressure to recognize the impact of deep learning, and the Hopfield nets and Boltzmann machines for which John Hopfield and Geoff Hinton were honored are "completely useless"

Tsarathustra

381,794 просмотров • 2 лет назад

"I think that's our only hope, for it being much starter than us, but still keeping us around." To co-exist with advanced AI, Geoffrey Hinton argues, we must design systems that develop strong maternal instincts towards humanity. Hinton, often called the 'Godfather of AI' was awarded the 2024 Nobel Prize in Physics "for foundational discoveries and inventions that enable machine learning with artificial neural networks". Watch the full event 'Our Future with AI':
0:59

"I think that's our only hope, for it being much starter than us, but still keeping us around." To co-exist with advanced AI, Geoffrey Hinton argues, we must design systems that develop strong maternal instincts towards humanity. Hinton, often called the 'Godfather of AI' was awarded the 2024 Nobel Prize in Physics "for foundational discoveries and inventions that enable machine learning with artificial neural networks". Watch the full event 'Our Future with AI':

The Nobel Prize

39,628 просмотров • 11 месяцев назад

➡️ 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 просмотров • 24 дней назад

➡️Machine Learning from Scratch, part 51: dropout. At every training step each hidden neuron flips its own coin and about half of them are switched off, a new random half every step. No neuron can lean on its neighbours, so the knowledge spreads over many neurons, and only patterns that survive these small tests get stronger. After training, dropout is switched off and all neurons work together, like the average of many smaller networks that share the same weights. The code (dropout_reel.py, 16 lines of numpy) trains the same 1-64-1 tanh network twice on the same 12 noisy points of sin(3x), 60,000 steps of plain gradient descent, once the plain way and once with inverted dropout (chance one half, survivors multiplied by two). On 99 new points of the same curve the plain network ends at an error of 0.135 (its training error is almost zero, and its test error grows from 0.026 at step 100), the dropout network at 0.045: three times smaller, with a training error of 0.069. Sources: Hinton et al. 2012, arXiv:1207.0580; Srivastava et al. 2014, JMLR 15. #machinelearning #ai #coding #neuralnetworks #deeplearning
2:11

➡️Machine Learning from Scratch, part 51: dropout. At every training step each hidden neuron flips its own coin and about half of them are switched off, a new random half every step. No neuron can lean on its neighbours, so the knowledge spreads over many neurons, and only patterns that survive these small tests get stronger. After training, dropout is switched off and all neurons work together, like the average of many smaller networks that share the same weights. The code (dropout_reel.py, 16 lines of numpy) trains the same 1-64-1 tanh network twice on the same 12 noisy points of sin(3x), 60,000 steps of plain gradient descent, once the plain way and once with inverted dropout (chance one half, survivors multiplied by two). On 99 new points of the same curve the plain network ends at an error of 0.135 (its training error is almost zero, and its test error grows from 0.026 at step 100), the dropout network at 0.045: three times smaller, with a training error of 0.069. Sources: Hinton et al. 2012, arXiv:1207.0580; Srivastava et al. 2014, JMLR 15. #machinelearning #ai #coding #neuralnetworks #deeplearning

MachineLearningToGo

14,900 просмотров • 6 дней назад

➡️ Machine Learning from Scratch, part 15: a recommender, the kind of algorithm that picks what your feed shows you. Our app is simulated: 300 people and 60 videos. Every person has a hidden taste, every video has a hidden style, and each person has watched only about 12 of the 60 videos. All the algorithm ever sees is who liked which of the videos they watched. How it learns: every person gets two numbers, and every video gets two numbers. Think of them as arrows on a map. For every video a person has seen, the two arrows turn into a chance of a like with the sigmoid from part 2. The algorithm compares that chance with what really happened and nudges both arrows a little. That is gradient descent from part 8, and the whole method is called matrix factorisation. The results, all measured on the videos nobody showed it: After 25 rounds it guesses 85.0% of the hidden likes right. After 3000 rounds it gets 87.8% right. The best any method could do here is 91.1%, because some likes are just random. For every person it recommends the three unseen videos with the strongest match, and 98% of them are videos the person really likes. A random unseen video gets a like 51% of the time. The first person on the list watched only 5 videos. From those five it guessed the other 55 and got 51 of them right. We ran the same program on 20 different simulated apps, and it always landed between 84 and 88%. Real apps use hundreds of numbers for every person and every video, plus signals like watch time and skips. The core idea stays the same: learn numbers for people and for videos, and match them. #machinelearning #ai #recommendersystem #python #coding
2:19

➡️ Machine Learning from Scratch, part 15: a recommender, the kind of algorithm that picks what your feed shows you. Our app is simulated: 300 people and 60 videos. Every person has a hidden taste, every video has a hidden style, and each person has watched only about 12 of the 60 videos. All the algorithm ever sees is who liked which of the videos they watched. How it learns: every person gets two numbers, and every video gets two numbers. Think of them as arrows on a map. For every video a person has seen, the two arrows turn into a chance of a like with the sigmoid from part 2. The algorithm compares that chance with what really happened and nudges both arrows a little. That is gradient descent from part 8, and the whole method is called matrix factorisation. The results, all measured on the videos nobody showed it: After 25 rounds it guesses 85.0% of the hidden likes right. After 3000 rounds it gets 87.8% right. The best any method could do here is 91.1%, because some likes are just random. For every person it recommends the three unseen videos with the strongest match, and 98% of them are videos the person really likes. A random unseen video gets a like 51% of the time. The first person on the list watched only 5 videos. From those five it guessed the other 55 and got 51 of them right. We ran the same program on 20 different simulated apps, and it always landed between 84 and 88%. Real apps use hundreds of numbers for every person and every video, plus signals like watch time and skips. The core idea stays the same: learn numbers for people and for videos, and match them. #machinelearning #ai #recommendersystem #python #coding

MachineLearningToGo

88,400 просмотров • 1 месяц назад

➡️ Machine Learning from Scratch, part 31: the transformer. A transformer turns every token into a list of numbers, adds its position, and then lets every token look at the others. That step is called attention: each token has a query (what am I looking for), a key (what do I contain) and a value (what I pass on). The better a query matches a key, the more of that value flows in. Real transformers repeat this in many layers with several attention heads each. The design comes from the 2017 Google paper "Attention Is All You Need" by Ashish Vaswani and colleagues, first used for translation. A large language model is a transformer trained to predict the next token, again and again, on a huge amount of text. Our own run, nothing downloaded. attention.py is one self-attention layer in 16 lines of numpy. Every word gets a query, a key and a value of 8 numbers, starting random, trained from scratch with hand-written backpropagation on 192 made-up sentences like "the dog ate the carrot because it was hungry". The task: find out which pet "it" means. On 64 new test sentences "it" puts 99.95 percent of its attention on the right word on average (the lowest single sentence gets 99.9 percent; the program prints this as 100%). Before training it was 10 percent, spread evenly. The cat sentence from the video is one of the test sentences. Honest notes: our task is easy on purpose. Every sentence has exactly one pet, so "it" only has to learn to look for pets, and it learns that within the first pass. Real sentences with two candidates need more context, which is what many layers are for. Our small version has no positions, no scaling, no mask, one head, and only "it" asks a question. Every word of the test sentences also appears in training; the combinations are new. The 3D space in the video shows the real keys of our model, projected to three axes (two of them stretched so you can see them). The stacked layers are a sketch. #machinelearning #ai #transformer #python #llm
2:03

➡️ Machine Learning from Scratch, part 31: the transformer. A transformer turns every token into a list of numbers, adds its position, and then lets every token look at the others. That step is called attention: each token has a query (what am I looking for), a key (what do I contain) and a value (what I pass on). The better a query matches a key, the more of that value flows in. Real transformers repeat this in many layers with several attention heads each. The design comes from the 2017 Google paper "Attention Is All You Need" by Ashish Vaswani and colleagues, first used for translation. A large language model is a transformer trained to predict the next token, again and again, on a huge amount of text. Our own run, nothing downloaded. attention.py is one self-attention layer in 16 lines of numpy. Every word gets a query, a key and a value of 8 numbers, starting random, trained from scratch with hand-written backpropagation on 192 made-up sentences like "the dog ate the carrot because it was hungry". The task: find out which pet "it" means. On 64 new test sentences "it" puts 99.95 percent of its attention on the right word on average (the lowest single sentence gets 99.9 percent; the program prints this as 100%). Before training it was 10 percent, spread evenly. The cat sentence from the video is one of the test sentences. Honest notes: our task is easy on purpose. Every sentence has exactly one pet, so "it" only has to learn to look for pets, and it learns that within the first pass. Real sentences with two candidates need more context, which is what many layers are for. Our small version has no positions, no scaling, no mask, one head, and only "it" asks a question. Every word of the test sentences also appears in training; the combinations are new. The 3D space in the video shows the real keys of our model, projected to three axes (two of them stretched so you can see them). The stacked layers are a sketch. #machinelearning #ai #transformer #python #llm

MachineLearningToGo

80,100 просмотров • 1 месяц назад

One of the best papers that combine machine learning and video games. The neural network learns motion from raw human data. The motion in-game feels fluid and natural, and best part is you dont have to manually write motion state-machines.
0:41

One of the best papers that combine machine learning and video games. The neural network learns motion from raw human data. The motion in-game feels fluid and natural, and best part is you dont have to manually write motion state-machines.

mansin

198,161 просмотров • 2 лет назад

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 просмотров • 1 год назад

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):
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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 просмотров • 1 год назад

Geoffrey Hinton and John Hopfield react to winning the Nobel prize, highlighting the grave risks of AI. Hinton: "with respect to the existential threat of these things getting out of control and taking over, I think we're at a kind of bifurcation point in history ..."
2:59

Geoffrey Hinton and John Hopfield react to winning the Nobel prize, highlighting the grave risks of AI. Hinton: "with respect to the existential threat of these things getting out of control and taking over, I think we're at a kind of bifurcation point in history ..."

ControlAI

715,059 просмотров • 2 лет назад

Deep-Path: The entire Deep-ML catalog, organized into one structured learning path featuring nearly 2,000 machine learning coding problems and hands-on projects. Throughout the path, you'll work through the math, implement concepts from scratch using minimal libraries like NumPy or CUDA, and build projects step by step There's also Deep-Path Essentials, a shorter version with 400 core problems and projects organized into 10 sections, covering the fundamentals of machine learning
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Deep-Path: The entire Deep-ML catalog, organized into one structured learning path featuring nearly 2,000 machine learning coding problems and hands-on projects. Throughout the path, you'll work through the math, implement concepts from scratch using minimal libraries like NumPy or CUDA, and build projects step by step There's also Deep-Path Essentials, a shorter version with 400 core problems and projects organized into 10 sections, covering the fundamentals of machine learning

Deep-ML

65,491 просмотров • 26 дней назад

Machine Learning from Scratch, part 1. Part 2 adds three lines to this exact program and the line turns into a neuron. #machinelearning #python #gradientdescent #linearregression #neuralnetworks
1:34

Machine Learning from Scratch, part 1. Part 2 adds three lines to this exact program and the line turns into a neuron. #machinelearning #python #gradientdescent #linearregression #neuralnetworks

MachineLearningToGo

358,200 просмотров • 1 месяц назад

Geoffrey Hinton once noticed that the tellers at his bank kept changing. When he asked one of them why, the teller didn't know, only that they got moved around a lot. Hinton guessed the reason... if staff never work with the same people for long, it's much harder for a few of them to team up and cheat the bank. That thought became one of the most useful tricks in deep learning. While a neural network trains, you randomly switch off about half its neurons at every step. No neuron can lean on a particular partner, so each one has to learn something useful on its own. The trick is called dropout, and it helped AlexNet win the 2012 ImageNet contest, which set off the modern AI boom. It's still built into many of today's networks.
0:30

Geoffrey Hinton once noticed that the tellers at his bank kept changing. When he asked one of them why, the teller didn't know, only that they got moved around a lot. Hinton guessed the reason... if staff never work with the same people for long, it's much harder for a few of them to team up and cheat the bank. That thought became one of the most useful tricks in deep learning. While a neural network trains, you randomly switch off about half its neurons at every step. No neuron can lean on a particular partner, so each one has to learn something useful on its own. The trick is called dropout, and it helped AlexNet win the 2012 ImageNet contest, which set off the modern AI boom. It's still built into many of today's networks.

Mathelirium

275,071 просмотров • 20 дней назад

In 1958, the US Navy stood Frank Rosenblatt in front of a $2 million IBM computer and told reporters it was the embryo of a machine that would someday be conscious of its own existence. His entire idea fit on one line of math. In 1969, two MIT researchers published a single puzzle that killed the funding for a decade. The line of math survived anyway - it runs inside every neuron in every LLM deployed today, copied billions of times and stacked in layers. The formula is a weighted sum followed by a nonlinearity: step of W dot X plus B. Draw it geometrically and it's a straight line dividing space into ones and zeros. The learning rule nudges the weights whenever it's wrong. Minsky and Papert proved it could never solve XOR - four points on a square where no straight line separates the classes. The fix was stacking layers and training them with backpropagation, published in Nature in 1986 by Rumelhart, Hinton, and Williams. Hinton shared the 2024 Nobel Prize in Physics for it. Rosenblatt drowned in a boating accident in 1971 on his 43rd birthday, 15 years before the solution arrived. 5 minutes. Bookmark & watch today. The one line of 1958 math that every transformer, every LLM, and every GPU on earth still runs on.
5:23

In 1958, the US Navy stood Frank Rosenblatt in front of a $2 million IBM computer and told reporters it was the embryo of a machine that would someday be conscious of its own existence. His entire idea fit on one line of math. In 1969, two MIT researchers published a single puzzle that killed the funding for a decade. The line of math survived anyway - it runs inside every neuron in every LLM deployed today, copied billions of times and stacked in layers. The formula is a weighted sum followed by a nonlinearity: step of W dot X plus B. Draw it geometrically and it's a straight line dividing space into ones and zeros. The learning rule nudges the weights whenever it's wrong. Minsky and Papert proved it could never solve XOR - four points on a square where no straight line separates the classes. The fix was stacking layers and training them with backpropagation, published in Nature in 1986 by Rumelhart, Hinton, and Williams. Hinton shared the 2024 Nobel Prize in Physics for it. Rosenblatt drowned in a boating accident in 1971 on his 43rd birthday, 15 years before the solution arrived. 5 minutes. Bookmark & watch today. The one line of 1958 math that every transformer, every LLM, and every GPU on earth still runs on.

Deliriumm

11,638 просмотров • 1 месяц назад

Introducing Human Input Detection, a new machine-learning layer of the FACEIT Anti-Cheat that detects cheaters by their in-game actions, not by the software they use. It continuously learns from millions of matches and gets better with every one. Live August 05 with Season 9.
1:30

Introducing Human Input Detection, a new machine-learning layer of the FACEIT Anti-Cheat that detects cheaters by their in-game actions, not by the software they use. It continuously learns from millions of matches and gets better with every one. Live August 05 with Season 9.

FACEIT CS2

620,695 просмотров • 3 месяцев назад

➡️ Part 9 of learning ML code from scratch Six weights, one wrong answer, and not one of them knows it was at fault. Backpropagation is how they find out. Two questions guide the video: what even is backpropagation, and why do models need it? Step 1, the forward pass. Two numbers go into a tiny network, run through a hidden layer, and one number comes out. Ours says 0.700. We wanted 1.0, so square the difference and the loss is 0.09. That single number is how wrong the model is. Step 2, the question. Six weights produced that answer together. Which of them is to blame, and by how much? Step 3, the one idea. Change a single weight by a tiny amount and watch the error move. How much the error changes per unit of weight is the gradient of that weight. In the video you can see it happen: the weight wobbles and the error bar answers. Step 4, the shortcut. Every weight running into the same neuron starts from one shared number, the blame of that neuron. Multiply that blame by the value each weight carried and you have its gradient. One blame, computed once, and every weight into that neuron gets its own gradient from it. That is why backpropagation is fast enough to train anything at all. Step 5, backwards. The blame starts at the output and every layer hands its share to the layer in front of it, back through the same network the data came forward through. It goes layer by layer, and inside one layer every neuron is done together. Step 6, the update. The optimizer moves every weight against its own gradient, the whole weight matrix in one go, so no weight waits for its turn. All six gradients here are negative, so all six weights go up. After one step the answer reads 0.769. After sixty it reads 0.968 and the error is down to 0.001. The honest part: every gradient in the video was checked against the measured change of the error. Move that one weight, see what the error does, divide. Backprop and the measurement agree to nine decimal places, otherwise nothing would have rendered. #machinelearning #backpropagation #neuralnetworks #python #coding
2:18

➡️ Part 9 of learning ML code from scratch Six weights, one wrong answer, and not one of them knows it was at fault. Backpropagation is how they find out. Two questions guide the video: what even is backpropagation, and why do models need it? Step 1, the forward pass. Two numbers go into a tiny network, run through a hidden layer, and one number comes out. Ours says 0.700. We wanted 1.0, so square the difference and the loss is 0.09. That single number is how wrong the model is. Step 2, the question. Six weights produced that answer together. Which of them is to blame, and by how much? Step 3, the one idea. Change a single weight by a tiny amount and watch the error move. How much the error changes per unit of weight is the gradient of that weight. In the video you can see it happen: the weight wobbles and the error bar answers. Step 4, the shortcut. Every weight running into the same neuron starts from one shared number, the blame of that neuron. Multiply that blame by the value each weight carried and you have its gradient. One blame, computed once, and every weight into that neuron gets its own gradient from it. That is why backpropagation is fast enough to train anything at all. Step 5, backwards. The blame starts at the output and every layer hands its share to the layer in front of it, back through the same network the data came forward through. It goes layer by layer, and inside one layer every neuron is done together. Step 6, the update. The optimizer moves every weight against its own gradient, the whole weight matrix in one go, so no weight waits for its turn. All six gradients here are negative, so all six weights go up. After one step the answer reads 0.769. After sixty it reads 0.968 and the error is down to 0.001. The honest part: every gradient in the video was checked against the measured change of the error. Move that one weight, see what the error does, divide. Backprop and the measurement agree to nine decimal places, otherwise nothing would have rendered. #machinelearning #backpropagation #neuralnetworks #python #coding

MachineLearningToGo

210,600 просмотров • 1 месяц назад

Before an autonomous machine can act, it has to understand. A construction site is one of the most unpredictable environments imaginable. The terrain changes with every pass. Material piles shift. Other machines move nearby. No two digs are the same. For a human operator, reading that environment is second nature, built from years of experience and constant sensory feedback. Teaching a machine to do the same is one of the hardest problems in autonomy. Scene understanding is how we're working to solve it. Our system fuses LiDAR point clouds, camera feeds, and real-time terrain data into a continuous, three-dimensional picture of everything happening around the machine. It's a living model of the environment, updated moment to moment so the machine can make decisions grounded in what's actually in front of it.
0:31

Before an autonomous machine can act, it has to understand. A construction site is one of the most unpredictable environments imaginable. The terrain changes with every pass. Material piles shift. Other machines move nearby. No two digs are the same. For a human operator, reading that environment is second nature, built from years of experience and constant sensory feedback. Teaching a machine to do the same is one of the hardest problems in autonomy. Scene understanding is how we're working to solve it. Our system fuses LiDAR point clouds, camera feeds, and real-time terrain data into a continuous, three-dimensional picture of everything happening around the machine. It's a living model of the environment, updated moment to moment so the machine can make decisions grounded in what's actually in front of it.

Bedrock Robotics

33,148 просмотров • 6 месяцев назад