Transfer learning is a critical skill to gain from your machine learning course. Transfer Learning is the reuse of a trained model for a new problem. It allows you to reuse a machine-learning model to create new models. Instead of building ML models from scratch, you can transfer knowledge from an existing model.
Collecting large amounts of data every time to train a new model can be challenging. Sometimes you only have a limited amount of data. The limited data can make creating machine learning models with satisfactory performance difficult. This is where Transfer Learning comes into play. You can make high-performing models even with limited data availability and less training.
As enterprises adopt machine learning algorithms for automation, transfer learning becomes the solution to cut implementation time for new enterprises. No company wants to water their additional time and resources on building new ML models when they can reuse pre-trained models. As a result, transfer learning has become an essential skill for a career in Machine Learning.
You can learn transfer learning under the machine learning courses at Edvancer. Before that, let us help you understand more about transfer learning, its benefits, and its implementation.
Transfer Learning is the process of using a pre-trained model to build another model on a new but similar problem. A machine uses the knowledge gained from a previous task to address a new case. For example, the module of a classifier that recognises food in an image can be transferred to another classifier model to identify drinks.
This may sound simple, but Transfer Learning is a powerful tool. Traditional machine learning relies on large data sets that are not always easily accessible. However, transfer learning allows you to avoid such high amounts of data. A model already trained on a particular task can handle a similar new task with comparatively less data.
Transfer Learning brings several advantages to the machine learning field. From saving time and resources to improving the efficiency of ML models, Transfer Learning has many benefits as mentioned below:
You need a large amount of data to train machine-learning algorithms from scratch. However, transfer learning minimises the need for large data sets as the model uses the knowledge of a pre-trained model.
In traditional machine learning, you must build a model from scratch without any previous knowledge. But with transfer learning, you get a better initial model that can perform a few tasks even without training.
As transfer learning leverages a pre-trained model, you can achieve the desired performance much sooner. Though the performance is not higher (nor low) than the models created using traditional machine learning, you can save time in training.
Machine Learning models on complex tasks take a long time to train. But transfer learning allows you to use the same data sets and knowledge each time a similar model is required. You only need to put in your time and resources once to train a model.
Once you build a model for a particular task, you can share its knowledge across different models that perform similar tasks. The entire training process becomes more efficient and less time-consuming.
As the model is already trained with pre-existing knowledge, transfer learning provides a higher learning rate for the ML models. With better initial points and higher learning rates, the ML models created using transfer learning deliver more accurate outputs.
Here are a few steps to show how Transfer Learning works:
The first step of creating a model using the transfer learning technique is to obtain a pre-trained model. The number of layers to reuse depends on the type of problem.
You can find several relevant models by doing some necessary research. Software like Keras and Tensorflow provide various such models that can be used for transfer learning.
The base model is what you obtain from the pre-trained model. You can download the network weights to save extra time on training. The base model can have additional neurons in the output layers. In such a case, you can remove the output layer and replace it with another according to your requirements.
Once you create the base model, you can freeze the starting layers obtained from the pre-trained model. It will make your new model learn the basic features. Afterwards, add new layers of learning models specific to the task your ML model must perform.
The final output of a pre-trained model may still differ from the output you are looking for. You can train the new output layers to meet your requirements.
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Machine Learning is one of the most in-demand fields these days. Every business needs ML professionals having all the necessary skills, including transfer learning. If you want to become a Machine Learning engineer, you must be aware of transfer learning techniques.
You can explore more about Transfer Learning by enrolling in a Machine Learning course that covers all the important topics. Transfer Learning is covered under Deep Learning in Keras and TensorFlow. Make sure to check for this in the course curriculum before enrolling in any certification in Machine Learning or Artificial Intelligence.
Edvancer is one of the leading online learning platforms offering industry-driven education. You can find the following Machine Learning courses on Edvancer’s website:
These courses cover all the important aspects of machine learning. You get to learn about transfer learning and other topics in detail under the IBM professional certification and IIT Kanpur Advanced Certification. However, the ‘Certified Machine Learning with Python Expert’ course gives you a deep understanding of ML in Python.
Along with traditional online learning via video lectures, you can develop practical experience by working on assignments and industrial projects. The modes of learning by Edvancer are among the greatest features of these courses. You get two learning options to choose from, including online live classes and self-paced learning. You can choose any learning style that suits you.
Transfer Learning allows you to build ML models even with less availability of resources and time. Instead of building machine learning models from scratch, you can reuse pre-trained models to develop new models on similar tasks. It reduces the building time and improves the efficiency of ML models.2. What are the limitations of transfer learning?
One of the limitations of Transfer Learning is that it only works when you have a pre-trained model on a similar task. If you need to create an ML model with no similar model already existing, you will need to build it from scratch. However, several types of pre-trained models are available on software like Keras and TensorFlow.3. What are the types of transfer learning?
The types of transfer learning are – Domain adaption, Domain confusion, Lulti task learning, One-shot learning, and Zero-shot learning.Share this on