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This is the core of the book for many students, covering fundamental models. It begins by introducing artificial neurons and various network architectures, delves into the geometry of binary threshold networks, and then provides a detailed treatment of supervised learning. This includes foundational algorithms like Perceptrons and Least Mean Squares (LMS), followed by an in-depth exploration of the workhorse of deep learning, the backpropagation algorithm. The structure ensures that readers understand the theoretical underpinnings before moving to more complex topics. Neural Networks A Classroom Approach By Satish Kumar.pdf
