bookbestseller Posted July 2 Report Share Posted July 2 Machine Learning Algorithms in Depth by Vadim SmolyakovEnglish | August 27, 2024 | ISBN: 1633439216 | 328 pages | MOBI | 10 MbLearn 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 AutoencodersMachine 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.Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.About the technologyLearn 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 bookMachine 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-seriesAbout the readerFor machine learning practitioners familiar with linear algebra, probability, and basic calculus.About the authorVadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft.Table of ContentsPART 11 Machine learning algorithms2 Markov chain Monte Carlo3 Variational inference4 Software implementationPART 25 Classification algorithms6 Regression algorithms7 Selected supervised learning algorithmsPART 38 Fundamental unsupervised learning algorithms9 Selected unsupervised learning algorithmsPART 410 Fundamental deep learning algorithms11 Advanced deep learning algorithms[b]Uploady[/b]https://uploady.io/n6ogldp0fole/evg92.7zRapidGatorhttps://rg.to/file/38ec33568cf570f0c1cbdca7fd374618/evg92.7z.html[b]UploadCloud[/b]https://www.uploadcloud.pro/xqtqgm6tu8zv/evg92.7z.htmlFikperhttps://fikper.com/9r5ugfseVR/evg92.7zFreeDLhttps://frdl.io/dhdfz0xnljkv/evg92.7z Link to comment Share on other sites More sharing options...
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