What Is Transfer Learning?

Transfer learning is a technique that makes learning something new easier. For example, it might be easier to learn Spanish if you’re already fluent in Italian. The languages are similar in many ways, so if you understand the principles of Italian grammar, you may be better able to pick up the principles of Spanish grammar. This is because many of the things you learned can transfer to the new subject. 

When it comes to machine learning, transfer learning is a similar concept. One machine learning algorithm can complete a task, and a second machine learning algorithm can start with that knowledge to complete a second task. 

In this article, you’ll learn more about how transfer learning can benefit machine learning, the challenges of using this technique, and how to start a career using transfer learning. 

What is transfer learning in machine learning? 

In machine learning, transfer learning makes training an AI algorithm much faster. Many machine learning applications require an algorithm built for a specific purpose and the time it takes a computer scientist or engineer to train the algorithm—an often time-intensive process. With transfer learning, once the algorithm has completed the task and created a model, you can then use that completed model to gain an advantage in training a second algorithm to perform a related task. 

The transfer learning process is determined by exactly what information you need to transfer and how and when you will do so. Transfer learning appears in different types, depending on the size of the data set and how similar the first data set is to the second. Three examples of transfer learning in machine learning include:

• Inductive learning: The prior and current tasks are different from one another, and the machine learning algorithm will try to induce overall rules that work to structure the data in similar ways. 

• Transductive learning: The tasks are the same or similar, and the machine learning algorithm will try to determine what to do based on the outcome of the prior task. 

• Unsupervised learning: The data is unlabelled, and the second algorithm will process the new data similarly to the unsupervised learning accomplished by the first algorithm. 

What is transfer learning used for?

Transfer learning sometimes offers greater benefits than traditional machine learning techniques. Traditional machine learning requires significant time to train an AI algorithm with the training data. On the other hand, transfer learning makes it possible to get more value from that work by reusing the training for another algorithm, allowing you to do the work just once for both algorithms. 

Sometimes, obtaining the data you need for traditional machine learning might be difficult or expensive. With transfer learning, you can repurpose related or similar data to save time and money. 

Applications of transfer learning

Transfer learning is a new technology; data scientists and AI engineers are still discovering various ways to use this machine learning technique. Here are some of the current and potential applications for transfer learning in machine learning:

• Health care: With algorithms that use transfer learning, doctors and health care professionals can read medical images, such as MRIs, to detect anomalies like tumors. A research team recently developed a transfer learning algorithm that produced a model capable of identifying brains that were free of tumors. 

• Robot vision: Computer programmers can use transfer learning to develop machines that can sense their environment visually. Scientists could use this technology to develop further artificial capabilities for classifying and detecting objects. 

• Speech recognition: We can use transfer learning to improve our speech recognition systems. For example, if you train a model to understand speech in one language, you can use transfer learning to speed up the training time to understand speech in a second language. 

• Transportation: Transfer learning can help us understand major traffic systems, both by understanding traffic as an entity and by predicting the actions of individual drivers. We could also use transfer learning to predict which transportation mode people will use. 

Who uses transfer learning?

Professionals working in machine learning or artificial intelligence use transfer learning. Sometimes, job titles and their related responsibilities overlap, and you might see two job postings describing the same work with different titles. Below, we’ll look at three potential careers in artificial intelligence that work with transfer learning: AI engineers, AI research scientists, and data scientists. 

AI engineer

Average annual US salary: $115,749 [1]

Job outlook (projected growth from 2022 to 2032): 23 percent [2]

Education requirements: You’ll likely need a bachelor’s degree to become an AI engineer, typically in computer science. 

You will create, program, and train artificial intelligence algorithms as an artificial intelligence engineer. Although you will program machine learning algorithms, you’ll rarely write code directly. Rather, you will pull code from other sources and use APIs to create applications. To succeed as an AI engineer, you must understand programming languages, data science, and engineering. 

AI research scientist

Average annual US salary: $125,404 [3]

Job outlook (projected growth from 2022 to 2032): 23 percent [2]

Education requirements: AI research scientists typically need a master’s degree to work in this field. Potential areas of study include artificial intelligence and machine learning. 

As an artificial intelligence research scientist, you will use machine learning techniques to find solutions to problems for your company or organization. You will work with AI algorithms and data science to build models for solving the challenges you’re working on. In this field, you will likely work with a team of other researchers, engineers, or robotic professionals. To succeed as an AI research scientist, you will need to understand programming languages and algorithms and have mathematical, problem-solving, and data management skills. 

Data scientist

Average annual US salary: $120,534 [4]

Job outlook (projected growth from 2022 to 2032): 35 percent [5]

Education requirements: Data scientists commonly require a bachelor’s degree, typically in mathematics, statistics, computer science, or a related field. 

As a data scientist, you analyze large data sets to extract useful information. You will gather, maintain, process, and analyze data using algorithms and statistical techniques. You'll communicate your findings to decision-makers after working with the data to draw conclusions. You will need logical thinking skills, statistical and mathematical skills, proficiency in data-oriented programming languages, and problem-solving abilities. 

Pros and cons of transfer learning

The biggest reasons to consider transfer learning are that it can save time, lower costs, and increase productivity. Transfer learning allows you to train an AI model with a much smaller data set. It also decreases the amount of time it takes computer scientists to train machine learning algorithms. In addition to the cost of labor, training a computer model requires computational costs, which can also be expensive. With transfer learning, you can reduce these costs by reusing your pre-trained models. 

Not only is transfer learning faster, but it can also be more accurate. Transfer learning leverages previous learning to create much larger data sets for the new model to access. 

You might encounter some situations where transfer learning is not the ideal method. For example, when the target tasks are very different from one another, it may not make sense to use transfer learning. Likewise, if your models are complex, you may find the two models are not compatible enough to get value from transfer learning. 

Another problem that can occur when using transfer learning is overfitting. This can happen when an AI model too closely resembles its training data and can’t adapt accurately to new data. To avoid this problem, you can set aside some training data to perform overfitting tests later in time. 

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