Book Image

Hands-On Transfer Learning with Python

By : Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh
Book Image

Hands-On Transfer Learning with Python

By: Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh

Overview of this book

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.
Table of Contents (14 chapters)


Cross Industry Standard Process for Data Mining (CRISP-DM) is one of the most popular and widely used processes for data mining and analytics projects. CRISP-DM provides the required framework, which clearly outlines the necessary steps and workflows for executing a data mining and analytics project, from business requirements to the final deployment stages and everything in between.

More popularly known by the acronym itself, CRISP-DM is a tried, tested, and robust industry standard process model followed for data mining and analytics projects. CRISP-DM clearly depicts the necessary steps, processes, and workflows for executing any project, right from formalizing business requirements to testing and deploying a solution to transform data into insights. Data science, data mining, and ML are all about trying to run multiple iterative processes to extract insights and information from data. Hence, we can say that analyzing data is truly both an art as well as a science, because it is not always about running algorithms without reason; a lot of the major effort involves understanding the business, the actual value of the efforts being invested, and proper methods for articulating end results and insights.

Data science and data mining projects are iterative in nature to extract meaningful insights and information from data. Data science is as much art as science and thus a lot of time is spent understanding the business value and the data at hand before applying the actual algorithms (these again go through multiple iterations) and finally evaluations and deployment.

Similar to software engineering projects, which have different life cycle models, CRISP-DM helps us track a data mining and analytics project from start to end. This model is divided into six major steps that cover from aspects of business and data understanding to evaluation and finally deployment, all of which are iterative in nature. See the following diagram:

CRISP-DM model depicting workflow for ML projects

Let's now have a deeper look into each of the six stages to better understand the CRISP-DM model.

Business understanding

The first and the foremost step is understanding the business. This crucial step begins with setting the business context and requirements for the problem. Defining the business requirements formally is important to transform them into a data science and analytics problem statement. This step also used to set the expectations and success criteria for both business and data science teams to be on the same page and track the progress of the project.

The main deliverable of this step is a detailed plan consisting of major milestones, timelines, assumptions, constraints, caveats, issues expected, and success criteria.

Data understanding

Data collection and understanding is the second step in the CRISP-DM framework. In this step we take a deeper dive to understand and analyze the data for the problem statement formalized in the previous step. This step begins with investigating the various sources of data outlined in the detailed project plan previously. These sources of data are then used to collect data, analyze different attributes, and make a note of data quality. This step also involves what is generally termed as exploratory data analysis.

Exploratory data analysis (EDA) is a very important sub-step. It is during EDA we analyze different attributes of data, their properties and characteristics. We also visualize data during EDA for a better understanding and uncovering patterns that might be previously unseen or ignored. This step lays down the foundation for the coming step and hence this step cannot be neglected at all.

Data preparation

This is the third and the most time-consuming step in any data science project. Data preparation takes place once we have understood the business problem and explored the data available. This step involves data integration, cleaning, wrangling, feature selection, and feature engineering. First and the foremost is data integration. There are times when data is available from various sources and hence needs to be combined based on certain keys or attributes for better usage.

Data cleaning and wrangling are very important steps. This involves handling missing values, data inconsistencies, fixing incorrect values, and converting data to ingestible formats such that they can be used by ML algorithms.

Data preparation is the most time-consuming step, taking over 60-70% of the overall time taken for any data science project. Apart from data integration and wrangling, this step involves selecting key features based on relevance, quality, assumptions, and constraints. This is also termed as feature selection. There are also times when we have to derive or generate features from existing ones. For example, deriving age from date of birth and so on, depending upon the use case requirements. This step is termed as feature engineering and is again required based on use case.


The fourth step or the modeling step is where the actual analysis and ML takes place. This step utilizes the clean and formatted data prepared in the previous step for modeling purposes. This is an iterative process and works in sync with the data preparation step as models/algorithms require data in different settings/formats with varying set of attributes.

This step involves selecting relevant tools and frameworks along with the selection of a modeling technique or algorithms. This step includes model building, evaluation, and fine-tuning of models, based on the expectations and criteria laid down during the business understanding phase.


Once the modeling step results in a model(s) that satisfies the success criteria, performance benchmarks, and model evaluation metrics, a thorough evaluation step comes into picture. In this step, we consider the following activities before moving ahead with the deployment stage:

  • Model result assessment based on quality and alignment with business objectives
  • Identifying any additional assumptions made or constraints relaxed
  • Data quality, missing information, and other feedback from data science team and/or subject matter experts (SMEs)
  • Cost of deployment of the end-to-end ML solution


The final step of the CRISP-DM model is deployment to production. The models that have been developed, fined-tuned, validated, and tested during multiple iterations are saved and prepared for production environment. A proper deployment plan is built, which includes details on hardware and software requirements. The deployment stage also includes putting in place checks and monitoring aspects to evaluate the model in production for results, performance, and other metrics.