<ul><li>CNNs (Convolutional Neural Networks) use filters or kernels to detect specific features in images.</li><li>Pooling layers reduce the size of feature maps, preventing overfitting and speeding up computation.</li><li>RNNs (Recurrent Neural Networks) have a hidden state, allowing them to process sequential data like time series and text.</li><li>Transformers use the attention mechanism to focus on relevant parts of the input and self-attention to understand word relationships.</li></ul>

Deep Dive into Deep Learning — The Nitty-Gritty Details! (Part 5)

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