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In essence, the Siamese network is a special architecture designed to learn similarity. It comprises two or more identical subnetworks. Each subnetwork processes a different input, and the outputs of the subnetworks are compared to determine their similarity. The shared-weights architecture guarantees that both subnetworks learn the same features. This helps to reduce the number of parameters and enables the network to generalize better. It's like having two identical twins learning the same thing – even if they see different things, their understanding aligns due to their shared experience and knowledge. The Siamese network's approach is particularly effective in scenarios where direct comparisons between inputs are necessary. This includes various applications, such as identifying if two images depict the same person or if two pieces of text convey the same meaning. The original paper's contribution was not just the architecture, but also the insightful use of a contrastive loss function. This function enables the network to learn to pull similar inputs closer together in the embedding space while pushing dissimilar inputs further apart. This approach enables the network to effectively learn a similarity metric, which is critical for many real-world applications. The impact of this paper is undeniable, as it continues to inspire advancements in the field of deep learning.
2. **Lagging Indicator:** PO adalah indikator *lagging*, yang berarti ia didasarkan pada data harga masa lalu. Ini berarti sinyal yang dihasilkan mungkin datang terlambat, terutama dalam pergerakan harga yang cepat.
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