Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Mastering Machine Learning Algorithms

You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

Giuseppe Bonaccorso

View More author details

Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Further reading

Dayan P., Abbott F. L., Theoretical Neuroscience, The MIT Press, 2003
Warner R., Applied Statistics, SAGE Publications, 2013
Sanger T. D., Single-Layer Linear Feedforward Neural Network, Neural Networks, 1989/2
Rubner J., Tavan P., A Self-Organizing Network for Principal-Components Analysis, Europhysics, Letters, 10(7), 1989
Principe J. C., Euliano N. R., Lefebvre W. C., Neural and Adaptive Systems: Fundamentals Through Simulation, Wiley 1997/1999
Willshaw D. J., Von Der Malsburg C., How patterned neural connections can be set up by self-organization, Proceedings of the Royal Society of London, B/194, N. 1117, 1976
Kohonen T., Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43/1, 1982
Kohonen T., Learning Vector Quantization, Self-Organizing Maps. Springer Series in Information Sciences, vol 30. Springer, 1995

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $19.99/month. Cancel anytime

Authors (2)

Bonaccorso

Bonaccorso

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.

See other products by Bonaccorso

Giuseppe Bonaccorso

Giuseppe Bonaccorso

Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.

See other products by Giuseppe Bonaccorso

Other recommended products

Related to this chapter

Hands-On Unsupervised Learning with Python

Hands-On Unsupervised Learning with Python

Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning for different real-world challenges.

Feb 2019 12h 52m

Hands-On Unsupervised Learning with Python

Hands-On Unsupervised Learning with Python

Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning for different real-world challenges.

Feb 2019 12h 52m

Hands-On Unsupervised Learning with Python

Hands-On Unsupervised Learning with Python

Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning for different real-world challenges.

Feb 2019 12h 52m

Machine Learning Algorithms

Machine Learning Algorithms

Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. This book will act as an entry point for anyone who wants to make a career in Machine Learning. It covers algorithms like Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering.

Aug 2018 17h 24m

Machine Learning Algorithms

Machine Learning Algorithms

Machine learning explores the study and construction of algorithms that can learn from, and make predictions on, data. This book will act as an entry point for anyone who wants to make a career in the field of Machine Learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering.

Jul 2017 12h 0m

Machine Learning Algorithms

Machine Learning Algorithms

Machine learning explores the study and construction of algorithms that can learn from, and make predictions on, data. This book will act as an entry point for anyone who wants to make a career in the field of Machine Learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering.

Jul 2017 12h 0m

Machine Learning Algorithms

Machine Learning Algorithms

Machine learning explores the study and construction of algorithms that can learn from, and make predictions on, data. This book will act as an entry point for anyone who wants to make a career in the field of Machine Learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering.

Jul 2017 12h 0m

Machine Learning Algorithms

Machine Learning Algorithms

Machine learning explores the study and construction of algorithms that can learn from, and make predictions on, data. This book will act as an entry point for anyone who wants to make a career in the field of Machine Learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering.

Jul 2017 12h 0m

Hands-On Markov Models with Python

Hands-On Markov Models with Python

This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.

Sep 2018 5h 56m

Hands-On Markov Models with Python

Hands-On Markov Models with Python

This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.

Sep 2018 5h 56m

Hands-On Markov Models with Python

Hands-On Markov Models with Python

This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.

Sep 2018 5h 56m

Hands-On Markov Models with Python

Hands-On Markov Models with Python

This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.

Sep 2018 5h 56m

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m