Neural networks are quickly becoming one of the most important topics in machine learning. Transfer learning, where you take a pre-trained model and retrain it for your own data set, is also becoming increasingly common.
Transfer learning is a form of machine learning where a model trained on one task or on one set of data (the source) can be reused and applied to a different task or a different data set (the target). The blog concentrates in discussing key benefits of using transfer learning and what it is capable of achieving.
So what is transfer learning?
It’s a set of techniques used in machine learning.
In particular, it’s a method used to train artificial neural networks on a set of data, and then use those trained networks to solve problems without retraining the network on the new problem.
An example of this would be using what you’ve learned about identifying dogs when training a network to classify cats.
Transfer learning is a type of machine learning where the pre-trained weights of a network are re-used using different data. This allows for faster and more efficient training time while still allowing the resulting neural network to work effectively with the new data.
The idea behind transfer learning is that by using a model that was trained on similar data, we can reduce our training time so that we can use the same model for multiple purposes. Even though this technique is already being used, there are many issues that are yet to be resolved. One major issue is that there is no standard way of measuring the performance of a network and there are high variations in performance across different datasets.
Due to these limitations, researchers have turned to using simpler models like linear regression instead of deeper models like multilayer perceptron.
Neural networks are a family of machine learning algorithms that enable complex data modeling and pattern recognition. While neural networks can be difficult to implement correctly, transfer learning can be used to dramatically simplify the development process.
Many applications of neural networks are derived from the idea of transfer learning. In this article, we’ll take a look at 5 key benefits of using transfer learning for building neural networks. we’ll take a look at 5 key benefits of using transfer learning for building neural networks.
- -When a neural network is trained using a large dataset, it can generalize and perform well on new data that was not part of the original training set
- -Transfer learning can be used to construct more effective classifiers
- -Transfer learning enables you to build more accurate and powerful models
- -Transfer learning makes it easier to build models from large datasets
- -Transfer learning is a powerful tool for model selection
.Some of the key advantages are :
1) Model weights are automatically transferred
Transfer learning requires a source model. This source model is a good place to start building a new target model because it already contains some of the information needed for the new model.
The weights from the source model can be used as-is, or they can serve as a starting point for further training on the target dataset.
2) Fewer parameters are needed for the target model
Transfer learning reduces the number of free parameters in the target network by using information from the source network. The new network has fewer free parameters, which makes it easier to train and results in better generalization performance.
3) Transfer learning can tolerate missing data
Transfer learning does not require all the data to be available at once. If some records are missing, you can use data from other records and/or use prior knowledge about your problem domain to fill in missing values. This is called imputation.
4) Transfer learning allows faster deployment
If you have deployed your trained source network, it is faster and cheaper to deploy a trained target network using transfer learning than to retrain an entirely new network using only the target data. Your networks will converge faster because they share weights with your previous networks. And, if you have an existing trained source
It can be difficult to develop an accurate network from scratch, and training from beginning to end is often time-consuming and expensive. With transfer learning, it’s possible to start with a generic model and fine-tune it for specific needs.
This technique can help save time and money, because it’s often easier to start with a smaller or simpler model and then train it specifically for your needs rather than starting with a large, comprehensive model that would require significant training before it could handle your specific data set.
Affordable Data – From a business standpoint, Transfer Learning provides an extremely affordable way for businesses to conduct research with their data.
Using Transfer Learning, a business can lease or obtain datasets on topics outside of their current focus at a fraction of the cost. If the business decides to continue training further down the rabbit hole of training deep neural networks on curated datasets, they are able to transfer that data over and start improving accuracy immediately.
So why not use transfer learning? In simplest terms, it allows us to build upon the knowledge gained from previous tasks, thereby saving a lot of time and effort. It becomes a matter of creating the best base model and then improving that model over time by adding new layers and increasing the capacity of the model. Hope this article at MlDots.
