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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 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 engineer. 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 time you’re done reading, you’ll know how major algorithms work under the hood—and be a better machine learning practitioner for it. about the reader For intermediate 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. 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. 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 Algorithm s in Depth youll explore practical implementations of dozens of ML algorithms 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, youll learn the fundamentals of Bayesian inference and deep learning. Youll 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 theyre put into action. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. 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, youll go from math-first principles to a hands-on implementation in Python. Youll 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 time youre done reading, youll know how major algorithms work under the hoodand be a better machine learning practitioner for it. About the reader For intermediate 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. 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. Develop a mathematical intuition for how machine learning algorithms work so you can improve model performance and effectively troubleshoot complex ML problems. 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. For each category of algorithm, you’ll go from math-first principles to a hands-on implementation in Python, exploring dozens of examples from across all the fields of machine learning. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics. 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 Copyright_2023_Manning_Publications welcome 1_Machine_Learning_Algorithms 2_Markov_Chain_Monte_Carlo 3_Variational_Inference 4_Software_Implementation 5_Classification_Algorithms 6_Regression_Algorithms 7_Selected_Supervised_Learning_Algorithms 8_Fundamental_Unsupervised_Learning_Algorithms 9_Selected_Unsupervised_Learning_Algorithms 10_Fundamental_Deep_Learning_Algorithms 11_Advanced_Deep_Learning_Algorithms Appendix_A._Further_Reading_and_Resources Appendix_B._Answers_to_Exercises