English | 2022 | ISBN: 9781633439214 | 131 pages | PDF,EPUB | 24.33 MB

Develop a mathematical intuition for how machine learning algorithms work so you can improve model performance and effectively troubleshoot complex ML problems.

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 -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 probability-based 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

Fully understanding how machine learning algorithms function is essential for any serious ML eeer. This vital knowledge lets you modify algorithms to your specific needs, understand the tradeoffs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.

about the book

Machine Learning Algorithms in Depth dives deep into the how and the why of machine learning algorithms. For each category of algorithm, you’ll go from math-first principles to a hands-on implementation in Python. You’ll explore dozens of examples from across all the fields of machine learning, including finance, computer vision, NLP, and more. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics. By the you’re done reading, you’ll know how major algorithms work under the hood—and be a better machine learning practitioner for it.