Graph-Powered Machine Learning
| Price | $49.99 - $61.66
|
| Rating |   |
| Author | Alessandro Negro |
| Publisher | Manning |
| Published | 2021 |
| Pages | 496 |
| Language | English |
| Format | Paper book / ebook (PDF) |
| ISBN-10 | 1617295647 |
| ISBN-13 | 9781617295645 |
Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients!
Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems.
Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you'll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.
- Alessandro Negro
3 5 4
Similar Books
Machine Learning with PyTorch and Scikit-Learn
by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili
Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and examples, the book covers all the...
Price: $35.99 | Publisher: Packt Publishing | Release: 2022
Practical Machine Learning for Computer Vision
by Valliappa Lakshmanan, Martin Görner, Ryan Gillard
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a gr...
Price: $59.54 | Publisher: O'Reilly Media | Release: 2021
Quantum Machine Learning: An Applied Approach
by Santanu Ganguly
Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research.The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learnin...
Price: $44.74 | Publisher: Apress | Release: 2021
by Akhil Wali
Clojure for Machine Learning is an introduction to machine learning techniques and algorithms. This book demonstrates how you can apply these techniques to real-world problems using the Clojure programming language.It explores many machine learning techniques and also describes how to use Clojure to build machine learning systems. This bo...
Price: $29.99 | Publisher: Packt Publishing | Release: 2014
by Nick Pentreath
Apache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease of programming and a flexible, expressive, ...
Price: $29.99 | Publisher: Packt Publishing | Release: 2015
by Patrick R. Nicolas
The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering designs, biometrics, and trading strategies, to detection of genetic anomalies.The book begins with an introduction to the...
Price: $35.99 | Publisher: Packt Publishing | Release: 2014
F# for Machine Learning Essentials
by Sudipta Mukherjee
The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs.If you want to learn how to use F# to build machine learning systems, then this is th...
Price: $31.99 | Publisher: Packt Publishing | Release: 2016
Building Machine Learning Systems with Python, 2nd Edition
by Luis Pedro Coelho, Willi Richert
Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learn...
Price: $39.99 | Publisher: Packt Publishing | Release: 2015