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Individual Neuron: Neural Network A neuron in a neural network performs a weighted sum of inputs, adds a bias, and applies an activation function like sigmoid, ReLU, or tanh, introducing non-linearity. This output helps the neuron learn and represent patterns in the data.

ₕₐₘₚₜₒₙ — e/acc

171,935 subscribers

47,467 görüntüleme • 2 yıl önce •via X (Twitter)

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ₕₐₘₚₜₒₙ — e/acc profil fotoğrafı

ₕₐₘₚₜₒₙ — e/acc2 yıl önce

3 minute data science.

Kiril profil fotoğrafı

Kiril2 yıl önce

I'd love to know how this sequence of mathematical methods was decided upon?

Max profil fotoğrafı

Max2 yıl önce

Full video

WikiBonsai profil fotoğrafı

WikiBonsai2 yıl önce

"neuron (neural network)" tldr: "A neuron in a neural network is a computational unit that processes input, applies an activation function, and produces output, mimicking biological neurons' functionality in artificial intelligence systems."

Shubhranshu Arya profil fotoğrafı

Shubhranshu Arya2 yıl önce

Such a beautiful and easy to understand visualisation😍

π profil fotoğrafı

π2 yıl önce

💯

ewe profil fotoğrafı

ewe2 yıl önce

@diegopatas_ obrigado, agora este perfil aparece com mais frequência para mim, estou até ponderando em seguir hahahahha

I'm Bob profil fotoğrafı

I'm Bob1 yıl önce

part two please 🙏

Adarsh Kumar profil fotoğrafı

Adarsh Kumar1 yıl önce

Where can I find the full video?

Scott Somerville profil fotoğrafı

Scott Somerville1 yıl önce

Can I use this model to describe a set of human interactions? Can something like this model describe the interaction of accounts here on X?

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Hugo Larochelle explains how a neuron computes its output: first, it calculates the preactivation, the sum of the bias and weighted inputs, and then applies an activation function.

Hugo Larochelle explains how a neuron computes its output: first, it calculates the preactivation, the sum of the bias and weighted inputs, and then applies an activation function.

tetsuo

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The neuron. weighted sums, a bias, a nonlinear bend.

The neuron. weighted sums, a bias, a nonlinear bend.

tetsuo

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I've solidified a neural net perceptron into a physical circuit. 100 billion of these is ChatGPT. f(x)= wx+b = 1*1 + 1.5 = 2.5 output = weight * input + bias You can change the input, weight and bias and see the output neuron update. Learning ML can be fun!

I've solidified a neural net perceptron into a physical circuit. 100 billion of these is ChatGPT. f(x)= wx+b = 11 + 1.5 = 2.5 output = weight input + bias You can change the input, weight and bias and see the output neuron update. Learning ML can be fun!

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Introducing Continuous Thought Machines New Blog: Modern AI is powerful, but it’s still distinct from human-like flexible intelligence. We believe neural timing is key. Our Continuous Thought Machine is built from the ground up to use neural dynamics as a powerful representation for intelligence. Thought takes time, and reasoning is a process. Biological brains inspire us with their complex neural activity, where neural timing is critical to intelligence. We’re exploring how to bring that power to AI. The Continuous Thought Machine (CTM) incorporates neuron-level temporal processing and neural synchronization, moving beyond current AI limitations. Our approach has two core innovations: (1) neuron-level temporal processing, where each neuron uses unique parameters to process a history of incoming signals for fine-grained temporal dynamics, and (2) neural synchronization, used as a direct latent representation to modulate data and produce outputs, encoding information directly in the timing of neural activity. Learn more about our approach: Interactive Report: Full Paper: GitHub :

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Neuron cell communication Neurons connect to other neurons and transmit information via neurites. These cultured neurons also extend several neurites to connect with neighboring neurons, forming a neural network.

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Implemented a visualization for the neural network today. This is a neural network being trained to learn the snake game in a terminal. It's built in Rust using Ratatui More input details in thread

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We often hear that machine learning models "learn patterns in data". But what does that actually look like in geometry? If you dropped a little elastic mesh into a cloud of points and let it learn, how would it fold itself to match the shape of the data? In this scene we watch a self-organizing map...a simple unsupervised neural model...learn the shape of a two-dimensional dataset arranged in a spiral arm. On top of this, we lay down a square grid of neurons whose weights live in the same plane. At the start, this grid is just a flat net floating across the cloud...it knows nothing about the structure underneath. Learning is a repeated game: pick a random data point, find the neuron whose weight is closest, and then nudge that neuron and its neighbours toward the point. Do this again and again, while slowly shrinking how far the neighbourhood influence spreads. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML

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Mathelirium

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A neural network can begin as a flat sheet and learn the shape of hidden data A self-organizing map turns learning into geometry. Each data point pulls one winning neuron toward it, but nearby neurons move too, and so the whole lattice bends without losing its neighborhood structure. The strange part is that the network is not given the roll shape. It discovers the shape through competition and local cooperation. Paper: Self-Organized Formation of Topologically Correct Feature Maps Authors: Teuvo Kohonen Year: 1982

A neural network can begin as a flat sheet and learn the shape of hidden data A self-organizing map turns learning into geometry. Each data point pulls one winning neuron toward it, but nearby neurons move too, and so the whole lattice bends without losing its neighborhood structure. The strange part is that the network is not given the roll shape. It discovers the shape through competition and local cooperation. Paper: Self-Organized Formation of Topologically Correct Feature Maps Authors: Teuvo Kohonen Year: 1982

Mathelirium

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Hopfield recall Spiking Neural network now this one is cool isn't it? -open source, link in comment- This is a Spiking Neural Network (SNN) that can learn and recall patterns through Hebbian learning, similar to a Hopfield network but using biologically-inspired spiking dynamics rather than energy-based settling.

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neuron activation ?

neuron activation ?

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*neuron activation*

neuron activation

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Inspired by Andrej Karpathy's practice of tinkering with smaller GPTs to gain intuition, I've been training small neural nets to play this simple 2d game I created. On the left is a visualization of the neural net firing in real time. Built with Claude, Three.js, and WebGPU and using JAX from Google DeepMind. I’m piping the neuron activation into Ableton as MIDI to trigger Serum 2.

Inspired by Andrej Karpathy's practice of tinkering with smaller GPTs to gain intuition, I've been training small neural nets to play this simple 2d game I created. On the left is a visualization of the neural net firing in real time. Built with Claude, Three.js, and WebGPU and using JAX from Google DeepMind. I’m piping the neuron activation into Ableton as MIDI to trigger Serum 2.

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Neuron activation

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doorman neuron activation

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We often hear that Machine Learning models learn patterns in data. But what does that actually look like in Geometry? If you dropped a little elastic mesh into a cloud of points and let it learn, how would it fold itself to match the shape of the data? In this scene we watch a Self-Organizing Map (SOM), a simple unsupervised neural model, learn the shape of a 3D datasets l, one static and the other dynamic. On top of this, we lay down a square grid of neurons whose weights live in the same plane. At the start, this grid is just a flat net floating across the cloud. It knows nothing about the structure underneath. Learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, and then nudge that neuron and its neighbours toward the point. Do this again and again, while slowly shrinking how far the neighbourhood influence spreads. Python code is available for Subscribers. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML

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Andrej Karpathy summarizes how neural networks function, including the process of using a loss function in training and applying backpropagation for optimizing the network parameters through gradient descent.

Andrej Karpathy summarizes how neural networks function, including the process of using a loss function in training and applying backpropagation for optimizing the network parameters through gradient descent.

tetsuo

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We often hear that Machine Learning models learn patterns in data. But what does that look like in geometry? Picture dropping an elastic mesh into a cloud of points and letting it adapt. How would it bend, stretch, and settle so it matches the shape hiding in the data? In this scene we watch a self-organizing map(SOM)...a classic unsupervised neural model...learn a 2D dataset arranged like a spiral arm. Over the points we place a square grid of neurons whose weights live in the same plane. At the start it’s just a flat net drifting across the cloud. No clue what the structure is. Fir the SOM, learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, then nudge that neuron and its neighbours toward the point. Repeat, repeat, repeat...while gradually shrinking how wide the neighbourhood influence spreads. The result is satisfying...the grid stops being a grid and turns into a coordinate sheet wrapped onto the spiral. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML #SelfOrganizingMap #Topology

We often hear that Machine Learning models learn patterns in data. But what does that look like in geometry? Picture dropping an elastic mesh into a cloud of points and letting it adapt. How would it bend, stretch, and settle so it matches the shape hiding in the data? In this scene we watch a self-organizing map(SOM)...a classic unsupervised neural model...learn a 2D dataset arranged like a spiral arm. Over the points we place a square grid of neurons whose weights live in the same plane. At the start it’s just a flat net drifting across the cloud. No clue what the structure is. Fir the SOM, learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, then nudge that neuron and its neighbours toward the point. Repeat, repeat, repeat...while gradually shrinking how wide the neighbourhood influence spreads. The result is satisfying...the grid stops being a grid and turns into a coordinate sheet wrapped onto the spiral. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML #SelfOrganizingMap #Topology

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