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DFS, BFS, UCS, and A* in PacmanImplementing graph search algorithms in the Berkeley Pacman framework. DFS, BFS, UCS, A*, plus heuristics for corners and food.
8 min -
RBF Networks for Function ApproximationImplementing a Radial Basis Function network with K-Means clustering and LMS training to approximate a noisy sinusoidal function.
6 min -
Backpropagation and the N-Bit Parity ProblemImplementing a multi-layer perceptron from scratch to solve the N-bit parity problem, and analyzing the effects of learning rate and momentum.
5 min -
Perceptron Learning, the XOR Problem, and Gradient DescentApplying the perceptron learning rule, confronting the XOR limitation, designing multi-layer networks, and connecting gradient descent to LMS.
5 min -
McCulloch-Pitts Neurons and Linear SeparabilityDesigning M-P neurons for conditional logic, building binary adders from neural networks, and exploring the limits of linear separability.
4 min -
BiLSTM-CNN-CRF for Sequence Labeling: A TutorialA PyTorch tutorial for the ACL 2016 paper on End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF, presented at ICML 2018.
13 min -
Deep Learning Methods for Quotable TextExploring what makes a sentence memorable using deep learning, achieving significant improvements over traditional linguistic feature approaches.
9 min
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