{"product_id":"machine-learning-algorithms-in-depth-9781633439214","title":"Machine Learning Algorithms in Depth","description":"\u003cb\u003eLearn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eFully understanding how machine learning algorithms function is essential for any serious ML engineer. In \u003ci\u003eMachine Learning Algorithms in Depth\u003c\/i\u003e you'll explore practical implementations of dozens of ML algorithms including: \u003cp\u003e\u003c\/p\u003e- Monte Carlo Stock Price Simulation\u003cbr\u003e - Image Denoising using Mean-Field Variational Inference\u003cbr\u003e - EM algorithm for Hidden Markov Models\u003cbr\u003e - Imbalanced Learning, Active Learning and Ensemble Learning\u003cbr\u003e - Bayesian Optimization for Hyperparameter Tuning\u003cbr\u003e - Dirichlet Process K-Means for Clustering Applications\u003cbr\u003e - Stock Clusters based on Inverse Covariance Estimation\u003cbr\u003e - Energy Minimization using Simulated Annealing\u003cbr\u003e - Image Search based on ResNet Convolutional Neural Network\u003cbr\u003e - Anomaly Detection in Time-Series using Variational Autoencoders \u003cp\u003e\u003c\/p\u003e \u003ci\u003eMachine Learning Algorithms in Depth\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003ePurchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e- Monte Carlo stock price simulation\u003cbr\u003e - EM algorithm for hidden Markov models\u003cbr\u003e - Imbalanced learning, active learning, and ensemble learning\u003cbr\u003e - Bayesian optimization for hyperparameter tuning\u003cbr\u003e - Anomaly detection in time-series \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For machine learning practitioners familiar with linear algebra, probability, and basic calculus. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eVadim Smolyakov\u003c\/b\u003e is a data scientist in the Enterprise \u0026amp; Security DI R\u0026amp;D team at Microsoft. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e PART 1\u003cbr\u003e 1 Machine learning algorithms\u003cbr\u003e 2 Markov chain Monte Carlo\u003cbr\u003e 3 Variational inference\u003cbr\u003e 4 Software implementation\u003cbr\u003e PART 2\u003cbr\u003e 5 Classification algorithms\u003cbr\u003e 6 Regression algorithms\u003cbr\u003e 7 Selected supervised learning algorithms\u003cbr\u003e PART 3\u003cbr\u003e 8 Fundamental unsupervised learning algorithms\u003cbr\u003e 9 Selected unsupervised learning algorithms\u003cbr\u003e PART 4\u003cbr\u003e 10 Fundamental deep learning algorithms\u003cbr\u003e 11 Advanced deep learning algorithms\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eVadim Smolyakov\u003c\/strong\u003e is a data scientist in Enterprise \u0026amp; Security DI R\u0026amp;D team at Microsoft. He is a former PhD student in AI at MIT CSAIL with research interests in Bayesian inference and deep learning. Prior to joining Microsoft, Vadim developed machine learning solutions in the e-commerce space.\u003c\/p\u003e\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":50911430574354,"sku":"9781633439214","price":73.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_d0dd15f5-21f6-4617-801b-cab85953fc12.jpg?v=1738741127","url":"https:\/\/surprise-castle.myshopify.com\/products\/machine-learning-algorithms-in-depth-9781633439214","provider":"Surprise Castle","version":"1.0","type":"link"}