In this blog post, we'll discuss a key innovation in sequence-to-sequence model architectures: the attention mechanism. This architecture innovation dramatically improved model performance for sequence-to-sequence tasks such as machine translation and text summarization. Moreover, the success of this attention mechanism led to the seminal
Let's say you want to deploy a recommender system at your company. A typical architecture might include a set of inference servers to run your embedding and ranking models, an approximate nearest neighbor index to select a set of candidate items that match your query, a database to retrieve features
When evaluating a standard machine learning model, we usually classify our predictions into four categories: true positives, false positives, true negatives, and false negatives. However, for the dense prediction task of image segmentation, it's not immediately clear what counts as a "true positive" and,
In this post, I'll discuss commonly used architectures for convolutional networks. As you'll see, almost all CNN architectures follow the same general design principles of successively applying convolutional layers to the input, periodically downsampling the spatial dimensions while increasing the number of feature maps.
Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learning. Specifically, we'll design a neural network architecture such that we impose a bottleneck in the network which forces a compressed knowledge representation of the original input.