Binary Classification in Supervised Learning
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Using an artificially generated dataset about two centers, I form a basic Neural Network and introduce L1 and L2 regularization, loss functions, and gradient descent.
Read HereRNNs, GRUs, and LSTMs
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I simulate a sine wave with noise and then explore RNN, GRU, and LSTM architectures from scratch as a form of denoising.
Read HereTransformer Architecture
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After discussing Query, Key, Value matrix initialization, I explain the attention process using a feedforward network to learn a saw wave.
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