Book Image

40 Algorithms Every Programmer Should Know

By : Imran Ahmad
5 (2)
Book Image

40 Algorithms Every Programmer Should Know

5 (2)
By: Imran Ahmad

Overview of this book

Algorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works. You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.
Table of Contents (19 chapters)
1
Section 1: Fundamentals and Core Algorithms
7
Section 2: Machine Learning Algorithms
13
Section 3: Advanced Topics

Transfer Learning

Over the years, many organizations, research groups, and individuals within the open source community have perfected some complex models trained using gigantic amounts of data for generic use cases. In some cases, they have invested years of effort in optimizing these models. Some of these open source models can be used for the following applications:

  • Object detection in video

  • Object detection in images

  • Transcription for audio

  • Sentiment analysis for text

Whenever we start working on training a new machine learning model, the question to ask ourselves is this: instead of starting from scratch, can we simply customize a well-established pre-trained model for our purposes? In other words, can we transfer the learning of existing models to our custom model so that we can answer our business question? If we can do that, it will provide...