Recent developments in deep learning have seen significant success by incorporating what’s been coined as pre-training. This new approach to AI goes beyond just having a network learn how to recognize objects, it also teaches the system other knowledge or skills like language processing.
By starting with larger networks that can perform well at image recognition tasks, we are able to use these trained weights to initialize another task. By doing this repeatedly, the algorithm learns how to do both tasks simultaneously.
This article will go into more detail about one of the main components of pre-training, question answering (QA). QA is an easy way to prove the effectiveness of unsupervised pretraining since you don’t need answers for any specific questions. You can simply ask the model something general and see if it applies the right concepts on its own!
Unsupervised pretraining has become increasingly popular due to its simplicity. There isn’t much requirement for large datasets or expensive computational power, making it more accessible than before. In fact, some companies make their pretrained models available online free of cost!
In this article, I’ll be sharing my top picks for pretrained QA models that anyone can download and try out for themselves. Hopefully you’ll find one that inspires you to add this technique to your arsenal!
Pretrained Question Answering Models
Give yourself around 30 minutes to train each model.
History of deep learning
Over the past few years, we have seen an explosion in the use of computer vision and speech recognition technology. Some applications are obvious, like using facial recognition to unlock phones or chatbots for social media platforms!
But what many people don’t realize is that most of this progress was made by studying how human brains work.
Trained neural networks now perform far better than humans at tasks such as object classification and understanding of natural language.
Deep neural nets can be trained to do these things without any kind of pre-training. This has led to some impressive results, but also lots of criticism due to worries about overfitting and biased predictions.
Unsupervised pretraining is still very common, though. Why? Because it works really well and seems like a good idea.
In this article, I will discuss why unsupervised pretraining is important for modern day AI research and whether it is actually helpful or not.
Disclaimer: The content in this article should not be used to make decisions around educational programs or to determine if an employer will accept the individual as a professional. Educational settings vary widely worldwide even within a country. Information here should simply be taken with a large grain of salt.
Why Is Pretraining Important For Neural Networks?
Neural network architectures typically start off with what’s called “unsupervised pretraining.
Advantages of deep learning
Recent developments in artificial intelligence depend heavily on advanced computational architectures that can be trained to perform complex tasks using large amounts of data.
The more data you have, the better your computer will learn!
Using this strategy, AI systems are able to apply concepts and algorithms thousands or even millions of times until they’re perfect. This is how most software works these days — think about all of the apps and tools (like Google Maps) that use math formulas to do their work.
By having computers “learn” from past experiences rather than being programmed explicitly for a given task, researchers are able to create machines with increasingly sophisticated abilities.
However, there comes a limit to how much data we have available to us. So, how can we get around this? A popular solution is pre-training.
What is pretraining?
Pretraining means starting off by giving an algorithm lots of unstructured information and then training it on the targeted task.
This approach was first used back in 1980 when scientists would teach dogs new tricks before moving onto teaching them to follow commands. They found that if you start working with inexperienced puppies, they’ll eventually pick up some basic behaviors like rolling over and sitting.
After establishing those fundamentals, you can move into more complicated lessons such as “sit” and “rollover.” In other words, while trick training is fun, it really helps advance canine knowledge.
Disadvantages of deep learning
One major disadvantage of using very large neural networks to train your models is that you need a lot of data to feed the network. If there are not enough examples, then the network will not be able to learn general rules about images.
Another drawback is that some concepts in computer science do not easily translate into the mathematical language used by artificial neurons. This can make it difficult to apply certain theories directly onto neural networks.
A third problem with overfitting occurs when the model becomes too dependent on the dataset it was trained on. When you test this model on new datasets or domains, it may perform well but cannot be generalized to other tasks.
With all these limitations, we must be careful how much overtraining happens during pre-training.
Examples of deep learning
Recent developments in computer vision and natural language processing (NLP) use what’s been coined as “deep learning.” This is an automated way to teach computers how to perform specific tasks, such as recognizing objects or writing sentences.
The key ingredient behind this technology is something called pre-training. Before teaching the system how to do its job, it goes through an initial stage where it learns some basic concepts that it can then apply to your task.
For example, if you wanted to train the system to recognize cats, it would be exposed to many, many examples of different types of animals before being taught what makes a cat look like.
A similar concept applies to NLP. By laying down lots of context about how people speak, the system will learn important rules about grammar and vocabulary.
With both of these systems now fully trained, they can be applied to new tasks. That’s why there are so many applications for AI — we’re still in the early stages of development!
Unsupervised pretraining has become increasingly popular in recent years. Rather than having to spend hours and hours manually labeling data, students can simply let the software figure out what needs doing on its own.
In this article, we’ll discuss the benefits of unsupervised pretraining and explore two easy ways to add it to your existing projects.
Deep learning models
Recent developments in deep neural networks have led to significant improvements in accuracy for many computer vision tasks, such as object recognition and natural language processing. These so called “deep” neural network architectures are typically built by starting with an input layer that receives features from the environment, then applying multiple layers of computation followed by an output layer that produces the class or prediction.
The first few layers of the architecture learn low level concepts like edges, lines, and shapes, while later stages hone in on more complex structures such as objects and languages.
By incorporating these earlier concepts into their model, researchers have been able to improve the performance of several classes of problems including image classification, question answering, and speech recognition.
However, one commonality across all of these applications is that they require large amounts of data to work effectively. This can be expensive to collect if done manually which often times is not feasible. An example of this would be collecting images with annotated examples for training an image classification algorithm.
That is where pre-training comes in!
Pre-training is an important step in the development process of state of the art algorithms. By initializing the parameters of the network using pretrained weights obtained through previous research, you reduce the need for costly supervised fine tuning which could potentially overfit the dataset used during downstream testing.
Types of deep learning
Recent developments in the field of artificial intelligence (AI) have led to different types or categories of AI. Some use very specific functions, whereas others do not!
One such category is what’s been referred to as “deep learning.” This term applies to algorithms that learn complex patterns from data, much like humans do.
By incorporating features into the algorithm that allow for more complicated pattern recognition, this technology has rapidly advanced over the past few years. Companies are now using it to create new products and services, and you can find it applied across various industries.
There are some who argue that because these systems are relying less on defining mathematical equations or logic structures, they go beyond the boundaries we set for AI.
That being said, there are reasons why pre-training neural networks before applying supervised training is so important. Let’s look at some examples.
A popular technique in deep learning is called transfer learning. With this method, you start with a network that has been trained on large datasets for other tasks and then you use the network as a basis to train your new task.
A common example of this is when someone trains a computer vision model on images of dogs. You can then use the learned concepts like shadows, shape, or texture to identify not only dogs but also pictures of cats or birds.
By using these concepts in different ways, the algorithm will learn how to recognize all three. This is an important part of modern day AI because it allows computers to apply knowledge from one area to something totally unrelated.
It’s kind of like having a general understanding of human anatomy. Even if you never studied medicine, you could still understand the basics of what makes people feel good by looking at their bodies. By applying such knowledge to another field, you get more insights into the way things work.
This concept was first described in a paper published back in 1989 called “Transferring Knowledge Between Similar Domains.” Since then, it has become one of the most powerful techniques in artificial intelligence.
How does unsupervised pre-training help deep learning?
One of the most important concepts in neural networks is what’s called layer-wise pretraining. Layer-wise pretraining means starting with an already-trained task (like language or image classification) and using that as initial state for a new, more complex task like object recognition.
By “state” we mean the internal variables of the network — things like neurons and weights. By having a well-functioning internal system, you can assume some basic ones (like adding numbers together), then use those to create something much harder.
One way this concept was first applied effectively was seen by Alexey Dosovskiy and Ruslan Salakhutdinov at Google. They coined the term ‘pretrained’ when they used it for natural language processing (NLP).
That method sets parts of the NLP model – like word embeddings or sentence encoders – as learned, which helps initialize the rest of the model. Because these components are trained separately from the main classifier, they can be tuned independently, improving overall performance.
This approach has since been adapted for almost every area of computer science beyond NLP. There have even been attempts to make it interactive! That is, instead of just training once and then testing, you can continually test the models and learn from their mistakes. This is sometimes referred to as lifelong learning.