With the increasing popularity of Machine Learning these days almost everyone is aware of the basic machine learning techniques and methodologies. What people are not aware of are some advanced concepts of machine learning. This is the exact reason why, today, we thought of shining some light on meta-learning and few-shot learning which are slightly advanced machine-learning concepts. So, let’s see what it is all about.
Meta Learning
Meta Learning essentially refers to “learning to learn”. Sounds confusing, right? Don’t worry, we’ll make sure that you are on the same page with us by the end of the article. So let’s see, what is machine learning? Machine learning is simply designing algorithms that will learn how to solve a problem based on prior data fed to the data models. Great. With meta-learning, you take this one step further. You not only tell your machine learning model that hey this is the piece of data and now this is how you can process it to give desired results but you also tell it, now whenever you get any other kind of data, you can figure out the method to deal with that in the same way. Mind you, don’t confuse it with machine learning.
With machine learning you only tell a machine to figure out future results for the “same” kind of data provided, but with meta-learning, you are basically telling a machine just the way you did this for “Task A” you should next time do it for “Task B”. We know it’s confusing, hence here’s an example for you. Imagine you’re teaching a robot not just how to do specific tasks like folding laundry or making coffee but also how to get better at learning new tasks. So, you’re not just saying, “Here’s how you fold a shirt,” but you’re also teaching the robot, “Here’s how you can figure out how to fold any piece of clothing you encounter.”
Now, to make it more interesting, you’re also teaching the robot how to learn more efficiently. It’s like saying, “Hey, when you encounter a new task, think about how you’ve learned things before and use that experience to adapt quickly.”
So, meta-learning is like teaching the robot not just what you know but also how to learn and get better at learning new things on its own. It’s learning to learn to learn, and it gets pretty clever at it!
So now conclusively, we can say that meta-learning is about systems that not only learn (level 1) but also learn how to learn (level 2), and in some cases, even learn how to improve how they learn how to learn (level 3) and so on. It’s layers upon layers of learning sophistication!
Few-Shot Learning
Let us now see what is few-shot learning. Few-shot learning is actually an application of meta-learning where we need a model to make very accurate predictions provided that the data fed into the system is significantly less. The few-shot learning paradigm necessarily doesn’t mean that the input data given is less, it could also mean that very few examples or instances of each class or category are provided to the system.
A whole new machine learning paradigm had to be created because traditional machine learning models only work when provided with supervised and mapped datasets, and that too, not accurate always. Few-shot learning aims to address this challenge by training models that can effectively learn from a small number of examples per class. This small number is often referred to as the “shot” or “shots” in the few-shot learning context.
Few-shot learning is of various kinds.
One-Shot Learning
A machine learning model that is trained to recognize or classify instances of a particular class based on only one example.
Few-Shot Learning (K-Shot)
This model is provided with a small number (K) of examples per class during training.
Zero-Shot Learning:
The model is expected to generalize to classes that were not seen during training. This kind of few-shot learning is the hardest to implement.
Vector Representations in Meta-Learning
In meta-learning, the concept of embedding examples into a vector space is common. Each example (or task) is represented as a point in a high-dimensional space, and the model learns how to navigate this space effectively.
What is Vector Search?
To understand what is this whole talk of vector search about, we first need to have a look at what exactly is a vector. Vector is a mathematical term meaning a representation of data in a multi-dimensional space. These vectors are used to represent various types of data, such as text, images, or any other structured or unstructured information. Vector Search is an algorithm that searches for information in a database by mapping each data item to a vector representation of itself. The key innovation behind vector search lies in these vectors capturing not just the raw data but also the relationships and similarities between data items.
How does vector search work?
Now that we have an idea of what big data and vector search is, let us see how it exactly works.
Vector search engines — known as vector database, semantic, or cosine search — find the nearest neighbors to a given (vectorized) query.
There are basically three methods to the vector search algorithm, let us discuss each of them one by one.
Vector Embedding
Wouldn’t it be simple to store data in simply one form? Thinking about it, a database having data points in one fixed form will make it so much easier and more efficient to carry out operations and computations on the database. In vector search, vector embedding is how one can do so. Vector embeddings are the numeric representation of data and related context, stored in high dimensional (dense) vectors.
Similarity Score
Another method under vector search that simplifies comparing two datasets is the similarity score. The idea of similarity score is that if two data points are similar their vector representation will be similar as well.
ANN Algorithm
The ANN algorithm is yet another method to account for the similarity between two datasets. The reason why the ANN algorithm is efficient is because it sacrifices perfect accuracy in exchange for executing efficiently in high dimensional embedding spaces, at scale.
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