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Mastering machine learning algorithms : expert techniques to implement popular machine learning algorithms and fine-tune your models [electronic resource]
Bonaccorso, Giuseppe
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| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | Mastering machine learning algorithms : expert techniques to implement popular machine learning algorithms and fine-tune your models/ Giuseppe Bonaccorso. [electronic resource]. |
| 개인저자 | Bonaccorso, Giuseppe,author. |
| 발행사항 | Birmingham, UK: Packt Publishing, 2018. |
| 형태사항 | 1 online resource (1 volume): illustrations. |
| ISBN | 9781788625906 1788625900 1788621115 9781788621113 |
| 요약 | Annotation |
| 초록 | Explore and master the most important algorithms for solving complex machine learning problems.Key FeaturesDiscover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and moreBook DescriptionMachine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks.If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.What you will learnExplore how a ML model can be trained, optimized, and evaluatedUnderstand how to create and learn static and dynamic probabilistic modelsSuccessfully cluster high-dimensional data and evaluate model accuracyDiscover how artificial neural networks work and how to train, optimize, and validate themWork with Autoencoders and Generative Adversarial NetworksApply label spreading and propagation to large datasetsExplore the most important Reinforcement Learning techniquesWho this book is forThis book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide. |
| 일반주제명 | Machine learning. Computer algorithms. Mathematical theory of computation. Artificial intelligence. Machine learning. Information architecture. Database design & theory. Computers --Intelligence (AI) & Semantics. Computers --Machine Theory. Computers --Data Modeling & Design. Computer algorithms. Machine learning. |
| 언어 | 영어 |
| 바로가기 | URL |
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