Applied machine learning using (51 Ergebnisse)

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paperback. Zustand: Very Good.

Soft cover. Zustand: New. This book discusses how to apply machine learning to real-world problems by utilizing real-world data. In this book, you will investigate data sources, become acquainted with data pipelines, and practice how machine learning works through numerous examples and case studies. The book begins with high-level concepts and implementation (with code!) and progresses towards the real-world of ML systems. It briefly discusses various concepts of Statistics and Linear Algebra. You will learn how to formulate a problem, collect data, build a model, and tune it. You will learn about use cases for data analytics, computer vision, and natural language processing. You will also explore nonlinear architecture, thus enabling you to build models with multiple inputs and outputs. You will get trained on creating a machine learning profile, various machine learning libraries, Statistics, and FAST API. Throughout the book, you will use Python to experiment with machine learning libraries such as Tensorflow, Scikit-learn, Spacy, and FastAI. The book will help train our models on both Kaggle and our datasets. …

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

hardcover. Zustand: Very Good. The copy shows minor external wear, but is in otherwise clean condition.

hardcover. Zustand: Good. Cover and edges may have some wear.

Paperback. Zustand: New. 1st ed. The item is brand new, never used or read. It's in perfect condition and may include supplements and/or access codes or come shrink-wrapped.

Paperback. Zustand: New. 1. The item is brand new, never used or read. It's in perfect condition and may include supplements and/or access codes or come shrink-wrapped.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Paperback. Zustand: Brand New. 404 pages. 9.75x6.75x1.00 inches. In Stock.

Zustand: New. 2018. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New. In English.

Paperback. Zustand: Brand New. new edition. 440 pages. 10.00x7.00x1.25 inches. In Stock.

Zustand: New.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Brand New. 337 pages. 9.00x7.00x0.75 inches. In Stock.

Zustand: New. In English.

Zustand: New. 2026. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New.

Zustand: New. 2025. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.- Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning- Set up and manage machine learning projects end-to-end- Build an anomaly detection system to catch credit card fraud- Clusters users into distinct and homogeneous groups- Perform semisupervised learning- Develop movie recommender systems using restricted Boltzmann machines- Generate synthetic images using generative adversarial networks.…

Kartoniert / Broschiert. Zustand: New. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-rea.