🧵 1/ Let's dive into Perceptron Learning! 🧠 A perceptron is the simplest type of artificial neural network used for binary classification tasks. It’s the building block of more complex neural networks. #MachineLearning #AI
🧵 2/ At its core, a perceptron makes decisions by weighing input signals, summing them up, and passing them through an activation function. If the result is above a certain threshold, it outputs one class; otherwise, it outputs another. #Perceptron #NeuralNetworks
🧵 3/ The perceptron learning algorithm adjusts the weights of the inputs to minimize the error in the classification. This is done through a process called supervised learning, where the perceptron learns from labeled training data. #SupervisedLearning
🧵 4/ Here's a step-by-step of the Perceptron Learning Algorithm: 1️⃣ Initialize weights and threshold (often to small random numbers). 2️⃣ For each training sample, compute the output. 3️⃣ Update weights based on the error (difference between predicted and actual).
🧵 5/ The weight update rule is: 𝑤=𝑤+Δ𝑤w=w+ΔwWhere: Δ𝑤=𝜂(𝑦−𝑦^)𝑥Δw=η(y−y^)xHere, 𝜂η is the learning rate, 𝑦y is the true label, 𝑦^y^ is the predicted label, and 𝑥x is the input feature. #MLMath
🧵 6/ Learning Rate (η) is crucial. It determines how much the weights are adjusted with each step. Too high, and you might overshoot the optimal solution. Too low, and learning will be too slow. Finding the right balance is key. #Hyperparameters
🧵 7/ Despite its simplicity, the perceptron can only solve linearly separable problems. This means it works well when the classes can be separated by a straight line (or hyperplane in higher dimensions). #Limitations
🧵 8/ For non-linearly separable problems, we need more advanced models like multi-layer perceptrons (MLPs), which introduce hidden layers and non-linear activation functions to capture complex patterns. #DeepLearning
🧵 9/ An interesting fact: The perceptron algorithm was invented by Frank Rosenblatt in 1958! It marked a significant milestone in the development of artificial intelligence. #HistoryOfAI
🧵 10/ In summary, the perceptron is a foundational concept in machine learning. Understanding it is crucial for grasping more complex neural network architectures. Happy learning! 🚀 #AI #MachineLearning #Perceptron
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