Compare CNN/RNN/LSTM and implement K-means

This problem has two independent parts: a conceptual comparison and an implementation task. Part A — Architecture concepts. Compare convolutional neural networks (CNNs) and recurrent neural networks (RNNs) when applied to: fixed-size $$224 \times 224$$ RGB images; text sequences whose length can vary. Include in your comparison: translation equivariance; locality; temporal ordering; parameter sharing; receptive-field growth; the ability to model long-range dependencies. Also…