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Embedding layer: Difference between revisions
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(Created page with "===Introduction== Machine learning often deals with high-dimensional vectors, which can be challenging to process and analyze directly. To solve this problem, machine learning models often incorporate an "embedding layer," which transforms the input data into a lower dimensional space where it's easier to interpret. This embedding layer plays a major role in many machine learning algorithms such as neural networks and has applications across various fields from natural l...") |
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===Introduction== | ===Introduction== | ||
Machine learning often deals with high-dimensional | [[Machine learning]] often deals with [[high-dimensional vector]]s for [[categorical feature]]s, which can be challenging to process and analyze directly. To solve this problem, [[machine learning models]] often incorporate an [[embedding layer]], which transforms the [[input]] data into a lower dimensional space where it's easier to interpret. This embedding layer plays a major role in many machine learning algorithms such as [[neural network]]s and has [[applications]] across various fields from [[natural language processing]] to [[image recognition]]. | ||
==What is an Embedding Layer?== | ==What is an Embedding Layer?== | ||
An embedding layer in a machine learning model is a type of layer that takes high-dimensional input data and maps it to a lower-dimensional space. This mapping is learned during training, creating | An embedding layer in a machine learning model is a type of layer that takes high-dimensional input data and maps it to a lower-dimensional space. This mapping is learned during training, creating [[embedding]]s, or compact representations of the original data which can be used as input for subsequent layers. | ||
In a neural network, the embedding layer is typically the initial stage and its purpose is to transform input data into something suitable for processing by subsequent layers. The output of this step is known as an | In a neural network, the embedding layer is typically the initial stage and its purpose is to transform input data into something suitable for processing by subsequent layers. The output of this step is known as an [[embedding]], which are low-dimensional vectors representing the input data. | ||
==Example== | |||
Earth supports approximately 300,000 flowering plant species. Let's say that tree species is a feature within your model. Your input layer should include a [[one-hot vector]] 300,000 elements. Perhaps ''fireweed'' could be described as this: | |||
An array of 300,000 elements is quite long. Training will be difficult if you don't include an embedding layer. This is due to multiplying 299,999 zeros. You might choose to have the embedding layers consist of 12 dimensions. The embedding layer will learn a new embedding pattern for each tree species. | |||
==How Does the Embedding Layer Work?== | ==How Does the Embedding Layer Work?== | ||
The embedding layer works by learning a mapping from high-dimensional input data to a lower-dimensional embedding space. This mapping is typically represented as a set of weights that are determined during model training. | The embedding layer works by learning a mapping from high-dimensional input data to a lower-dimensional [[embedding space]]. This mapping is typically represented as a set of [[weights]] that are determined during model training. | ||
Learning an embedding can be seen as a form of dimensionality reduction, where the aim is to capture the most significant features from input data in a lower-dimensional space. The embedding layer does this by recognizing patterns and relationships within input data that are pertinent for the task at hand, then creating embeddings that accurately replicate these relationships. | Learning an embedding can be seen as a form of dimensionality reduction, where the aim is to capture the most significant features from input data in a lower-dimensional space. The embedding layer does this by recognizing patterns and relationships within input data that are pertinent for the task at hand, then creating embeddings that accurately replicate these relationships. | ||