Can you generate a short video of how neuron works in machine learning?

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Welcome to understanding how neurons work in machine learning. A neuron is the fundamental building block of neural networks, inspired by biological brain cells. Each neuron receives multiple inputs, processes them using weights and bias, and produces an output through an activation function. Now let's see how the neuron processes inputs. Each input value is multiplied by its corresponding weight. For example, input zero point eight times weight zero point five equals zero point four. We do this for all inputs, then sum the results to get one point two one. Next, we add the bias term to our weighted sum. One point two one plus zero point two equals one point four one. Then we apply an activation function like ReLU, which returns the maximum of zero and the input. Since one point four one is positive, the output remains one point four one. In neural networks, multiple neurons are organized into layers. The input layer receives the raw data, hidden layers process the information, and the output layer produces the final result. Each neuron in one layer connects to all neurons in the next layer, allowing complex patterns to be learned. To summarize what we have learned: Neurons are the fundamental building blocks of neural networks. They process inputs using weights and bias, apply activation functions for non-linearity, and work together in layers to recognize complex patterns. Through training, the network learns by adjusting these weights to improve performance.