Название: Machine Learning Algorithms in Depth (Final Release)
Автор: Vadim Smolyakov
Издательство: Manning Publications
Год: 2024
Страниц: 328
Язык: английский
Формат: pdf (true)
Размер: 26.6 MB

Learn how Machine Learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.
Fully understanding how Machine Learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you'll explore practical implementations of dozens of ML algorithms including:

Monte Carlo Stock Price Simulation
Image Denoising using Mean-Field Variational Inference
EM algorithm for Hidden Markov Models
Imbalanced Learning, Active Learning and Ensemble Learning
Bayesian Optimization for Hyperparameter Tuning
Dirichlet Process K-Means for Clustering Applications
Stock Clusters based on Inverse Covariance Estimation
Energy Minimization using Simulated Annealing
Image Search based on ResNet Convolutional Neural Network
Anomaly Detection in Time-Series using Variational Autoencoders

Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting Machine Learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you'll learn the fundamentals of Bayesian inference and Deep Learning. You'll also explore the core data structures and algorithmic paradigms for Machine Learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they're put into action.

About the technology

Learn how Machine Learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods.

About the book

Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You'll especially appreciate author Vadim Smolyakov's clear interpretations of Bayesian algorithms for Monte Carlo and Markov models.

This book dives into the design of ML algorithms from scratch. Throughout the book, you will develop mathematical intuition for classic and modern ML algorithms and learn the fundamentals of Bayesian inference and deep learning as well as data structures and algorithmic paradigms in ML.

Understanding ML algorithms from scratch will help you choose the right algorithm for the task, explain the results, troubleshoot advanced problems, extend algorithms to new applications, and improve the performance of existing algorithms. What makes this book stand out from the crowd is its from-scratch analysis that discusses how and why ML algorithms work in significant depth, a carefully selected set of algorithms that I found most useful and impactful in my experience as a PhD student in machine learning, fully worked out derivations and implementations of ML algorithms explained in the text, as well as some other topics less commonly found in other ML texts.

After reading this book, you’ll have a solid mathematical intuition for classic and modern ML algorithms in the areas of supervised and unsupervised learning, and will have gained experience in the domains of core ML, natural language processing, computer vision, optimization, computational biology, and finance.

What's inside
Monte Carlo stock price simulation
EM algorithm for hidden Markov models
Imbalanced learning, active learning, and ensemble learning
Bayesian optimization for hyperparameter tuning
Anomaly detection in time-series

About the reader
This book was written for anyone interested in exploring Machine Learning algorithms in depth. It may prove invaluable to many different types of readers, including the following:
- Aspiring data scientists
- Entry- to principal-level data scientists
- Software developers seeking to transition to data science
- Data engineers seeking to deepen their knowledge of ML models
- Graduate students with research interests in ML
- Undergraduate students interested in ML

The prerequisites for reading this book include a basic level of programming skills in Python, and an intermediate level of understanding of linear algebra, applied probability, and multivariable calculus.

About the author
Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft.

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