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Grokking the Machine Learning Interview


# What is transfer learning?
Transfer learning is the task of using a pre-trained model and applying it to a new task, i.e., transferring the knowledge learned from one task to another. This is useful because the model doesn’t have to learn from scratch and can achieve higher accuracy in less time as compared to models that don’t use transfer learning.




## Motivation
The use of transfer learning in the machine learning domain has surged in the last few years. The following are the top reasons:


1. **Growth in the ML community and knowledge sharing**: The research and investments by top universities and tech companies have grown exponentially in the last few years and there is also a strong desire to share state-of-the-art models and datasets with the community. This allows people to utilize pre-trained models in a specific area bootstrap quickly.
2. **Common sub-problems**: Another key motivator is that many problems share common sub-problems, e.g., in all visual understanding and prediction areas, tasks such as finding edges, boundaries, and background are common sub-problems. Similarly, in the text domain, the semantic understanding of textual terms can be helpful in almost all problems where the user is represented by text terms, including search, recommendation systems, ads, etc.
3. **Limited supervised learning data and training resources**: Many real-world applications are still mapped onto supervised learning problems where the model is asked to predict a label. One key problem is the limited amount of training data available for models to generalize well. One key advantage of doing transfer learning is that we have the ability to start learning from pre-trained models, and hence, we can utilize the knowledge from similar domains. 

   Self-supervised learning models are able to utilize massive available datasets for text and image representation, e.g., Word2vec embedding models don't need any manual labels and can use the books and Wikipedia data to build a semantic understanding of terms effectively. Once we train a model for a certain representation, it can be utilized and help in many other supervised learning tasks.


    Transfer learning also optimizes training resources, and it helps teams that don’t have massive computing resources available. For instance, Google can train a BERT model on billions of examples with its massive computing power, but others are going to find it challenging to train similar optimized models. With transfer learning, we don't have to reinvest those resources and can just plug in the output of the BERT model or use it as a sub-model in our training process. We discussed this concept in detail earlier in our [embedding](https://www.educative.io/courses/grokking-the-machine-learning-interview/embeddings) lesson discussion.



An introduction to transfer learning, its importance, and applications.

Introduction

Practical ML Techniques/Concepts

Search Ranking

Feed Based System

Recommendation System

Self-Driving Car: Image Segmentation

Entity Linking System

Ad Prediction System

Transfer Learning

What is transfer learning?

Motivation