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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Fundamentals and Core Algorithms
Section 1: Fundamentals and Core Algorithms
Free Chapter
2
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
3
Data Structures Used in Algorithms
Data Structures Used in Algorithms
Exploring data structures in Python
Exploring abstract data types 
Summary
4
Sorting and Searching Algorithms
Sorting and Searching Algorithms
Introducing Sorting Algorithms
Introduction to Searching Algorithms
Practical Applications
Summary
5
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
6
Graph Algorithms
Graph Algorithms
Representations of graphs
Introducing network analysis theory
Understanding graph traversals
Case study – fraud analytics
Summary
7
Section 2: Machine Learning Algorithms
Section 2: Machine Learning Algorithms
8
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
9
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
10
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
11
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
12
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
13
Section 3: Advanced Topics
Section 3: Advanced Topics
14
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
15
Cryptography
Cryptography
Introduction to Cryptography
Understanding the Types of Cryptographic Techniques
Example – Security Concerns When Deploying a Machine Learning Model
Summary
16
Large-Scale Algorithms
Large-Scale Algorithms
Introduction to large-scale algorithms
The design of parallel algorithms
Strategizing multi-resource processing
Summary
17
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
18
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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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