Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: Perceptron Implementation

The video tutorial covers the implementation of a simple perceptron without an activation function or bias term. It begins with importing necessary packages like Numpy and PyTorch, followed by defining a weighted sum function using PyTorch tensors. The tutorial then tests the implementation with a sample dataset, initializes weights, and sets up for gradient descent. The video concludes with a discussion on future enhancements to build deeper neural networks.

How is the weight vector initialized in the perceptron implementation?

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What role does automatic differentiation play in updating the weight vector during training?

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Summarize the overall implementation process of a perceptron as described in the video.

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