English | 2024 | ISBN: 1633439216 | 328 pages | True PDF | 26.57 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. InMachine 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.

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
For machine learning practitioners familiar with linear algebra, probability, and basic calculus.

About the author

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

Table of Contents

PART 1
1 Machine learning algorithms
2 Markov chain Monte Carlo
3 Variational inference
4 Software implementation
PART 2
5 Classification algorithms
6 Regression algorithms
7 Selected supervised learning algorithms
PART 3
8 Fundamental unsupervised learning algorithms
9 Selected unsupervised learning algorithms
PART 4
10 Fundamental deep learning algorithms
11 Advanced deep learning algorithms