**Autor: Giuseppe Bonaccorso**

Broj strana: 576

ISBN broj: 9781788621113

Izdavač:
PACKT PUBLISHING

Godina izdanja: 2018.

Pregleda (30 dana / ukupno): **19 / 623**

Predlog za prevod

- Explore how a ML model can be trained, optimized, and evaluated
- Understand how to create and learn static and dynamic probabilistic models
- Successfully cluster high-dimensional data and evaluate model accuracy
- Discover how artificial neural networks work and how to train, optimize, and validate them
- Work with Autoencoders and Generative Adversarial Networks
- Apply label spreading and propagation to large datasets
- Explore the most important Reinforcement Learning techniques

Machine 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.

Giuseppe Bonaccorso is an experienced team leader/manager in AI, machine/deep learning solution design, management, and delivery. He got his MScEng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, cryptocurrencies, and NLP.

Chapter 1: Machine Learning Model Fundamentals

Chapter 2: Introduction to Semi-Supervised Learning

Chapter 3: Graph-Based Semi-Supervised Learning

Chapter 4: Bayesian Networks and Hidden Markov Models

Chapter 5: EM Algorithm and Applications

Chapter 6: Hebbian Learning and Self-Organizing Maps

Chapter 7: Clustering Algorithms

Chapter 8: Ensemble Learning

Chapter 9: Neural Networks for Machine Learning

Chapter 10: Advanced Neural Models

Chapter 11: Autoencoders

Chapter 12: Generative Adversarial Networks

Chapter 13: Deep Belief Networks

Chapter 14: Introduction to Reinforcement Learning

Chapter 15: Advanced Policy Estimation Algorithms

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