Catalog Search Results
Author
Pub. Date
[2019]
Language
English
Description
Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based...
Author
Pub. Date
2017.
Edition
First edition.
Language
English
Description
Learn how to construct machine learning and data analysis scalable for big data using H2O software, using sample data sets and several machine-learning techniques including deep learning, random forests, unsupervised learning and ensemble learning.
Author
Pub. Date
[2022]
Edition
1st ed.
Language
English
Description
Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. You'll start with an introduction to AI, where you'll learn the history of neural networks and what sets deep learning apart from other varieties...
Author
Pub. Date
2019.
Edition
First edition.
Language
English
Description
Create and implement AI- and ML-based features in your Swift apps for iOS, macOS, tvOS, and watchOS. With this practical book, programmers and developers of all kinds will find a one-stop shop for AI and machine learning with Swift. You'll learn how to build features that use powerful AI software to identify images, make predictions, generate content, make recommendations, and more. AI is increasingly essential for every developer, and you don;t...
51) Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python
Author
Pub. Date
2018.
Edition
Second edition, fully revised and updated.
Language
English
Author
Pub. Date
[2017]
Language
English
Description
"Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends...
Author
Pub. Date
2020.
Edition
Second edition.
Language
English
Description
Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You'll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10.
Author
Pub. Date
[2018]
Language
English
Description
Get up-to-speed with Microsoft's AI platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of "should" I build AI into my business, but more about "where" do I begin and how do I get started...
Author
Pub. Date
2016.
Language
English
Description
With this book you can get to grips with the concepts of machine learning through exciting real-world examples. Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning. Learn to build your own machine learning system with this example-based practical guide. If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is...
Author
Pub. Date
[2019]
Language
English
Description
Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R's syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics...
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