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What is Transfer Learning  and why it is used

 

Transfer learning is an active research topic in the field of machine learning. As the name suggests transfer learning is the art of conserving the amount of knowledge gained in solving one problem and then applying this saved knowledge to a new problem and doing a better job at it. This is a very important breakthrough in the field of machine learning and data sciences as data is the only fuel that powers machine learning algorithms. With the help of transfer learning even if the data is less we can still acquire the best results.This reduces the need for data related to the specific task we are dealing with. This reduces the need for data related to the specific task we are dealing with.

 

For this an example would be suppose a model which was earlier used for recognising bicycles will now recognise bikes.

 

Humans have an inherent ability to transfer knowledge across tasks. What we acquire as knowledge while learning about one task, we utilize in the same way to solve related tasks. The more related the tasks, the easier it is for us to transfer, or cross-utilize our knowledge.

 

Some simple examples would be,

  • Know how to ride a motorbike - Learn how to ride a car

  • Know how to play classic piano - Learn how to play jazz piano

  • Know math and statistics - Learn machine learning

Some of the most commonly used frameworks for transfer learning 

  • Pytorch

  • Keras

  • Tensorflow

  • theano

  • Pillow

  • numpy

  • pandas

  • openCV

  • jupyter notebook

Few applications of computer vision with transfer learning are given below:

  • Real world Simulations

  • gaming

  • Image Classification

  • Zero Short Translation

  • Text classification

  • Sentiment Analysis

  • Face Recognition

  • Object Recongnition

  • Gesture Recognition

  • Object tracking

Pretrained computer vision Models

  • Alexnet

  • vgg16-19

  • ResNet

  • GoogLeNet

  • SqeezeNet

  • InceptionV3,

  • ResNet,

  • MobileNet,

  • Xception,

  • InceptionResNetV2

  • Densenet

  • Facenet


 

Pretrained NLP Models 

 

Multi-Purpose NLP Models

  • ULMFiT

  • Transformer

  • Google’s BERT

  • Transformer-XL

  • OpenAI’s GPT-2


 

Word Embeddings

  • ELMo

  • Flair

Other Pretrained Models

  • StanfordNLP

  • Word2vec

  • sen2vec

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