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Understanding Transfer learning: For deep learning
In machine learning, huge data goes into training models. Many times the model becomes complex so the cost of model training increases at a higher rate. Models of machine learning which are complex can easily be prepared only if you have years of experience. The models are not created very efficiently if the machine learning and artificial intelligence engineers do not have a good experience. This is how to transfer learning is more significant for deep learning.
Transfer learning is the learning where one problem is solved and the model of machine learning is again used in providing the solution to the problem. The process of training speeds up and the performance is improved as in these models of machine learning a lot of time is consumed. This method basically improves the models learning and it decreases the usage of time and the learning process becomes more spontaneous for the particular task. The machine learning problems are generally solved easily.
There are 2 approaches for transfer learning which you can learn:
A selection process is the first step where models which are trained with large sets of data are selected, extraction of the feature is done and model is prepared then. The model which is prepared for one particular task can be easily used for the other model.
From the whole list of models, a pre-trained model is being chosen in this approach. The model which is selected can also be used for the other task, similar to the first approach which might have some corrections which are to be done.
Where transfer learning can be used?
Transfer learning is mainly used in two areas it is applications where training datasets need images and videos and second is when text is required.
When the algorithm has to access through images or videos, transfer learning has frequently used these places. The models which have used image datasets and transfer learning are google inception model and Microsoft Resnet model. A deep learning model should be leveraged which is used on the dataset of large images.
As a training dataset, in this learning, a large dataset of text is used for the main model and then again used for transfer learning. This transfer learning strategies which are used to train one game can also allow you in playing multiple games as well. Another example of transfer learning is in the analysis of sentiments. Feeds on the twitter are used for the sentiment analysis training whereas the same model is again used for other applications as well, which is movie reviews.
The main objective of transfer learning is:
It saves time and the efforts and people can easily be dependent on using the models which are already used for the 1st task. It is cost effective also which is involved in a high-cost GPU. The ultimate goal is the evolution of machine learning as per the possibility of human learning. While you are applying transfer learning on some models has a positive transfer, which decreases the performance of the model when it is used for some newer task. The transfer should never be negative when transfer learning is used for solving some problems.
This tool transfer learning has the power to solve the problem and achieve impressive results. There are several applications today which need models of machine learning which can easily transfer knowledge and can have access to the new solutions of machine learning.