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40 Algorithms Every Programmer Should Know

By : Imran Ahmad

3.8 (33)

40 Algorithms Every Programmer Should Know

3.8 (33)

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.

Preface

Preface

Who this book is for

What this book covers

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Section 1: Fundamentals and Core Algorithms

Section 1: Fundamentals and Core Algorithms

Free Chapter

Overview of Algorithms

Overview of Algorithms

What is an algorithm?

Specifying the logic of an algorithm

Introducing Python packages

Algorithm design techniques

Performance analysis

Validating an algorithm

Summary

Data Structures Used in Algorithms

Data Structures Used in Algorithms

Exploring data structures in Python

Exploring abstract data types

Summary

Sorting and Searching Algorithms

Sorting and Searching Algorithms

Introducing Sorting Algorithms

Introduction to Searching Algorithms

Practical Applications

Summary

Designing Algorithms

Designing Algorithms

Introducing the basic concepts of designing an algorithm

Understanding algorithmic strategies

Practical application – solving the TSP

Presenting the PageRank algorithm

Understanding linear programming

Practical application – capacity planning with linear programming

Summary

Graph Algorithms

Graph Algorithms

Representations of graphs

Introducing network analysis theory

Understanding graph traversals

Case study – fraud analytics

Summary

Section 2: Machine Learning Algorithms

Section 2: Machine Learning Algorithms

Unsupervised Machine Learning Algorithms

Unsupervised Machine Learning Algorithms

Introducing unsupervised learning

Understanding clustering algorithms

Dimensionality reduction

Association rules mining

Practical application– clustering similar tweets together

Anomaly-detection algorithms

Summary

Traditional Supervised Learning Algorithms

Traditional Supervised Learning Algorithms

Understanding supervised machine learning

Understanding classification algorithms

Understanding regression algorithms

Practical example – how to predict the weather

Summary

Neural Network Algorithms

Neural Network Algorithms

Understanding ANNs

The Evolution of ANNs

Training a Neural Network

Tools and Frameworks

Transfer Learning

Case study – using deep learning for fraud detection

Summary

Algorithms for Natural Language Processing

Algorithms for Natural Language Processing

Introducing NLP

BoW-based NLP

Introduction to word embedding

Using RNNs for NLP

Using NLP for sentiment analysis

Case study: movie review sentiment analysis

Summary

Recommendation Engines

Recommendation Engines

Introducing recommendation systems

Types of recommendation engines

Understanding the limitations of recommender systems

Areas of practical applications

Practical example – creating a recommendation engine

Summary

Section 3: Advanced Topics

Section 3: Advanced Topics

Data Algorithms

Data Algorithms

Introduction to data algorithms

Presenting data storage algorithms

Presenting streaming data algorithms

Presenting data compression algorithms

A practical example – Twitter real-time sentiment analysis

Summary

Cryptography

Cryptography

Introduction to Cryptography

Understanding the Types of Cryptographic Techniques

Example – Security Concerns When Deploying a Machine Learning Model

Summary

Large-Scale Algorithms

Large-Scale Algorithms

Introduction to large-scale algorithms

The design of parallel algorithms

Strategizing multi-resource processing

Summary

Practical Considerations

Practical Considerations

Introducing practical considerations

The explainability of an algorithm

Understanding ethics and algorithms

Reducing bias in models

Tackling NP-hard problems

When to use algorithms

Summary

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Using RNNs for NLP

An RNN is a traditional feed-forward network with feedback. A simple way of thinking about an RNN is that it is a neural network with states. RNNs are used with any type of data for generating and predicting various sequences of data. Training an RNN model is about formulating these sequences of data. RNNs can be used for text data as sentences are just sequences of words. When we use RNNs for NLP, we can use them for the following:

Predicting the next word when typing

Generating new text, following the style already used in the text:

Remember the combination of words that resulted in their correct prediction? The learning process of RNNs is based on the text that is found in the corpus. They are trained by reducing the error between the predicted next word and the actual next word.

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