The video tutorial covers different gradient descent methods: stochastic, mini batch, and batch gradient descent. It explains the bias term's role in neural networks, allowing hyperplanes to be positioned arbitrarily. The tutorial details how each gradient descent method works, their benefits, drawbacks, and computational requirements. Mini batch gradient descent is highlighted as a practical compromise. The video concludes with a preview of an animation and coding demonstration in the next video.

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