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Frank Rosenblatt, often cited as the Father of Machine Learning, photographed in 1960 alongside his most-notable invention: the Mark I Perceptron machine — a hardware implementation for the perceptron algorithm, the earliest example of an artificial neural network, est. 1943.
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How Biology Became the Foundation of Artificial Intelligence 🧠
At the core of neural networks is a simple idea inspired by the human brain.
A biological neuron receives signals through dendrites, integrates them in the cell body, and sends an impulse forward if the signal is strong enough.
An artificial neuron follows a similar logic, but mathematically.
Inputs are multiplied by weights, summed together, adjusted by a bias, and then passed through an activation function.
With a sigmoid activation function, the output is always between 0 and 1.
This means the neuron does not simply answer "yes" or "no".
It produces a smooth value that can be interpreted as confidence or probability. 📊
Before transformers, attention mechanisms, and large language models, this was the foundation: the perceptron and the activation function.
Modern AI begins at the smallest level, with one artificial neuron. ✨
El perceptron
Debería de escribir sin más una palabra tras otra palabra, pero es como si fuera una jaula auto perseptiva, que me arrojaba a una autoconstrucción quería encontrar algo nuevo pero todo me arroja a lo mismo, una y otra vez pensaba que estába fuera de esa jaula pero seguía dentro pensando que estaba fuera era muy raro en el sentido de que para comprender lo externo tenía que hacerlo interno asimilarlo descifrarlo para poder mirarlo, aunque esa explicación de lo quera externo era en realidad una conceptualización propia construida desde mi propio lenguaje, que no existen otras palabras que nunca antes haya pronunciado para comprender eso que era nuevo!!!, cuántas veces tendría que recorrer ese camino para dejar de ver lo que siempre veo...y si quisiera no percibirme y percibirme a la vez como desde fuera tendría que acceder a otros sentidos? Tendría algún caso...si no pudiera describir eso que pareciera ante mi como lo vería, como lo definiría, y si lo hiciera sería por qué ya estaba en esa jaula, y si no lo hiciera es porque yo ya no estaba en la misma prisión conceptual... O quizá estaría en una nueva jaula, quizá la percepción no implica necesariamente una definición, afirmación...
Curious about the Perceptron, the building block of neural networks? 🚀
In this video, we break down the Perceptron algorithm step-by-step — showing how it learns to classify data by mimicking how the human brain processes information. Perfect for beginners diving into machine learning and deep learning.
🎯 What You’ll Learn:
✅ What is a Perceptron? Understand its structure and how it works. ✅ How Perceptrons Learn: Explore weights, biases, activation functions, and decision boundaries. ✅ Hands-on Example: See a perceptron model solve a real classification problem. ✅ Where It’s Used: Discover real-world applications like binary classification and pattern recognition.
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In essence, a perceptron is an artificial neuron and represents the simplest form of an artificial neural network.
The week in AI: Google goes all out at I/O as regulations creep up
Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here’s a handy roundup of the last week’s stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own. This week, Google dominated the AI news cycle with a range of new products that launched at its annual I/O developer conference. They run the…
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