Understanding and Utilizing Embedding in ML: A Brief Overview
Machine Learning Models feed on data to train themselves. The higher its quality, the higher the probability of the model being a success. However, models understand only numbers, so how do you account for more complex input data such as text, images, and audio? That is where embedding machine learning comes in.
This blog will explore what embeddings are, explain how they work, and list down a few ways to use them to get the most out of your ML models.
What Are Embeddings in Machine Learning, and Why Are They Important?
Succinctly put, embedding numerical representations of objects such as text, images, audio, or videos that are designed in a manner that can be:
- Understood by machine learning models
- Contain sufficient parameters by which the ML model can discern similarities or patterns from them
In more complex languages, embeddings in models are low-dimensional numerical representations of higher-dimensional objects. The inherent benefit of embeddings in deep learning is that they allow machine learning models to identify patterns that would be humanly impossible to do.
ML embeddings are essential as they reduce the dimensionality and complexity of the data while preserving some of the semantic information.
For example, where the One-Hot Encoding method may require thousands of numbers to represent a single word along with its semantics, embeddings can do the same as vectors of a few hundred numbers.
Finally, once learned, embeddings also serve as features for other ML models, such as regressors or classifiers. This process, called feature embedding, allows models to discern patterns and relationships effectively rather than only trying to leverage raw data.
How do Embeddings Work?
ML embeddings work by using specialized algorithms called embedding models, which are trained on large amounts of data to create numerical representations of them in the form of vectors.
In the field of Mathematics, vectors are geometric objects with both magnitude and direction. They are represented as ordered pairs or lists of numbers, such as (x, y) or [x, y], where x and y represent their position in reference to the two axes.
In embeddings, vectors perform the same function. Take, for example, you are training an ML model to create embeddings to draw comparisons between certain movies based on three parameters, say, genre, its rating, and the year it was produced.
The vectors for the said movies would be represented as
A - [0.1, 0.6, 1989]
B - [0.1, 0.9, 2001]
C - [0.9, 0.1, 1956]
D - [0.7, 0.6, 2012]
If the algorithm were then asked to recommend a movie based on the user's preference (Movie A) based on the genre alone, it would measure the similarity between movies by calculating their cosine similarity, which ranges from -1 (opposite) to 1 (identical).
In the above example, moves A and B have similar vectors assigned to the parameter "Genre" (0.1); hence, the algorithm would recommend B as its best recommendation as opposed to C and D.
One of the most common types of embedding models is a neural network, which is a machine-learning model composed of layers of artificial neurons that can learn complex functions from data.
An embedded neural network takes an input, which can be an image or a movie, and passes it through multiple hidden layers that perform mathematical operations on it.
The output of the last layer is the embedding vector, a list of numbers representing the input.
Types of Embeddings in Machine Learning
There are various types of embeddings used in automated machine learning. The three most common are:
1. Categorical Embeddings
Categorical embeddings are a type of embedding that is used to represent categorical data in machine learning. The example in the previous section represents a categorical embedding.
Categorical embeddings are necessary as traditional machine learning algorithms cannot work directly with categorical data. This form of embedding is used in everything from NLP, recommender systems or computer vision.
2. Word Embeddings
Word embeddings are the foundations of Natural Language Processing (NLP) and are by far the most common types of embedding in use today. As the name suggests, this form of embedding focuses primarily on text-based inputs, with the primary focus being on capturing semantic information to allow learning models to understand and process natural language.
3. Image Embeddings
Just like word embedding, image embedding focuses on transforming images into numerical vectors. However, in this instance, the primary focus is capturing image parameters such as shape, color, texture, and spatial relationships, to name a few.
Convolutional Neural Networks (CNNs) are the most common deep learning model used to extract the features mentioned above whose output results in an image embedding.
Benefits of Utilizing Embeddings in ML
Embedding in Machine Learning offers myriad benefits when it comes to creating data for ML models, such as -
- Embedding models can generate vast and more compressive datasets that manual labeling just won't be able to match.
- They can also identify similarities, detect patterns, or capture latent features that may be far more complex than humanly possible or feasible to do so.
Examples of Embeddings in Real-world Applications
Embeddings in ML distill the essence of the data and play a key role in many practical applications, ranging from recommendation systems to natural language processing. Now, let's examine a few illustrative real-world examples.
Large Language Models
Large Language Models that are used for Generative AI tools, such as ChatGPT, use text embedding as their foundation to be able to capture the semantic meaning of words and phrases and then generate accurate and meaningful content based on real-time user-generated input.
Tesla, the pioneer of mass-manufactured semi-autonomous vehicles today, uses real-time image embedding for its Autopilot feature. This system uses the aid of eight cameras to capture the vehicle's surroundings in audio to enable the vehicle to perform functions such as lane keeping, lane change, cruise control, and parking itself.
Best Practices for Using Embeddings in ML
There are a few best practices to remember to get the best out of embeddings. The chief among them are:
Choosing the Right Embedding Technique
Different types of embeddings, such as NLP embedding, image embedding, or text embedding, use different techniques to generate them. Each of them, for example, SVD, Word2Vec, GloVe, and BERT, will perform differently on different data.
There are also other factors you need to consider when choosing an embedding method, such as the size of the input data and the end goal of the task at hand.
For example, Principal Component Analysis (PCA) is great for analyzing embedding by reducing dimensions and helping with visualization.
Similarly, BERT excels at capturing semantic and syntactic information of words in different contexts but, on the other hand, generates large and complex embeddings that are difficult to interpret.
Optimizing Embedding Dimension
Simply put, embedding dimensions are the number of features or parameters in an embedding vector. The higher the dimensions, the more comprehensive the data.
The general consensus with ML is that the higher the quality (quality in this instance referring to the abundance of semantic information) of the data, the better the performance of the ML model (hence the importance given to data governance frameworks in this field). That said, the higher the dimensional complexity, the more computational resources to run the model.
On the other hand, lowering the embedding dimension beyond a certain point will result in a loss of essential information that may affect the overall quality of the ML model.
You can use several methods to optimize the embedding sediments for a given data set and the tasks at hand, such as:
- Grid search or random search
- Bayesian optimization
- Embedding pruning
The key here is to strike the right balance between the expressiveness of the data and the efficiency (resources required) for a specific task.
Future Trends in Embeddings and ML
The embedding space is witnessing a plethora of advances that hold the potential to propel ML applications to the 4th dimension.
One key trend is the development of multimodal embeddings - an embedding model that learns from data that comes from multiple modalities, such as images, text, and audio.
Google Lens stands as a prime example that uses this embedding technique, which enables it to identify objects, landmarks, text, and more from images.
Interpretable embedding is another embedding technique that is gaining traction and holds tremendous promise in the years to come. The primary focus of this embedding technique is to make the output of embedded machine learning that is interpretable by humans.
Plenty of research is already in progress on its feasibility in healthcare.
Finally, there is a growing trend towards the development of real-time embeddings. Real-time embeddings can be generated and updated on the fly without needing to pre-train a model. The push towards self-driving cars stands as a prime example of where real-time embeddings hold tremendous value in the years to come.
Summing it Up
Embedding in machine learning is vital to model training. They are the gateway for machine learning models to interpret multidimensional and complex input data in a manner understandable to them. As the adoption of AI-based technology into every facet of modern life is on a steep upward graph, embedding in all its forms is and will continue to play a dominant role.
If you are already on the AI train and are looking for ways to harness the full potential of your data, Markov ML is the platform for you.
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