Embeddings are generated from deep learning models by converting input data, such as text, images, or audio, into a continuous vector space. This process involves passing the input through various layers of a neural network, where each layer extracts different features and representations. The final output of the model, often from one of the last layers, is a fixed-size vector that captures the essential characteristics of the input. For instance, in natural language processing, words or sentences can be transformed into embeddings that represent semantic meanings, allowing the model to understand relationships between them.
To illustrate, consider a simple example using a neural network for text data. First, the text is tokenized, and each token (like a word) is converted into an initial vector using techniques like one-hot encoding or pre-existing word embeddings like Word2Vec or GloVe. These vectors are then input to an embedding layer in the neural network, which further adjusts them through learned weights. The deeper the network layers, the more abstract the features become. As you move toward the output layer, the embeddings reflect complex relationships and contextual information, making them useful for various downstream tasks like classification or recommendation.
Another example can be seen in image processing tasks. When an image is input into a convolutional neural network (CNN), each convolutional layer extracts and compresses visual features such as edges, textures, or shapes. The final layers may combine these features into a compact vector representation of the image, which can then serve multiple purposes, such as matching similar images or classifying the content. Overall, whether for text or images, the goal of generating embeddings is to achieve a compact, informative representation of the original data, enabling the neural network to perform efficiently on tasks that require input understanding.