This article aims to be an attempt to cover theoretical concepts as well as demonstrate practical hands-on examples of deep learning applications in one place, given the information overload which is out there on the web. The case studies depicted here and their results are purely based on actual experiments which we conducted when we implemented and tested these models while working on our book: Hands on Transfer Learning with Python (details at the end of this article). Note: All the case studies will cover step by step details with code and outputs. We will look at transfer learning as a general high-level concept which started right from the days of machine learning and statistical modeling, however, we will be more focused around deep learning in this article. Case Study 2: Multi-Class Fine-grained Image Classification with Large Number of Classes and Less Data Availability.Case Study 1: Image Classification with a Data Availability Constraint.To be more specific, we will be covering the following. In this article, we will do a comprehensive coverage of the concepts, scope and real-world applications of transfer learning and even showcase some hands-on examples. Transfer learning is the idea of overcoming the isolated learning paradigm and utilizing knowledge acquired for one task to solve related ones.

The models have to be rebuilt from scratch once the feature-space distribution changes. These algorithms are trained to solve specific tasks. We transfer and leverage our knowledge from what we have learnt in the past!Ĭonventional machine learning and deep learning algorithms, so far, have been traditionally designed to work in isolation. In each of the above scenarios, we don’t learn everything from scratch when we attempt to learn new aspects or topics.

Deep learning francois chollet