{"product_id":"machine-learning-algorithms-in-depth","title":"Machine Learning Algorithms in Depth","description":"\u003cp\u003e\u003cb\u003eDevelop a mathematical intuition for how machine learning algorithms work so you can  improve model performance and effectively troubleshoot complex ML problems.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eIn \u003ci\u003eMachine Learning Algorithms in Depth\u003c\/i\u003e you’ll explore practical implementations of dozens of ML algorithms including:\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eMonte Carlo Stock Price Simulation\u003c\/li\u003e\n\u003cli\u003eImage Denoising using Mean-Field Variational Inference\u003c\/li\u003e\n\u003cli\u003eEM algorithm for Hidden Markov Models\u003c\/li\u003e\n\u003cli\u003eImbalanced Learning, Active Learning and Ensemble Learning\u003c\/li\u003e\n\u003cli\u003eBayesian Optimization for Hyperparameter Tuning\u003c\/li\u003e\n\u003cli\u003eDirichlet Process K-Means for Clustering Applications\u003c\/li\u003e\n\u003cli\u003eStock Clusters based on Inverse Covariance Estimation\u003c\/li\u003e\n\u003cli\u003eEnergy Minimization using Simulated Annealing\u003c\/li\u003e\n\u003cli\u003eImage Search based on ResNet Convolutional Neural Network\u003c\/li\u003e\n\u003cli\u003eAnomaly Detection in Time-Series using Variational Autoencoders\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\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 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout The Technology\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eFully 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.\u003cbr\u003e\u003c\/p\u003e\u003ch5\u003eAbout The Book\u003c\/h5\u003e\u003cp\u003e\u003ci\u003eMachine Learning Algorithms in Depth\u003c\/i\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003edives 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.\u003c\/p\u003e\u003ch5\u003eAbout The Author\u003c\/h5\u003e\u003cp\u003e\u003cb\u003eVadim Smolyakov\u003c\/b\u003e is a data scientist in the 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\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eeBook License\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eEnd-User Warranty and License Agreement  \u003c\/p\u003e\u003cp\u003e1. Grant of License  \u003c\/p\u003e\u003cp\u003eManning has authorized the download by you of an unrestricted number of copies of the electronic book (ebook) in any of the available formats. Manning grants you a nonexclusive, nontransferable license to use the ebook according to the terms and conditions herein. This License Agreement permits you to install the ebook on any and all your devices for your personal use only.  \u003c\/p\u003e\u003cp\u003e2. Restrictions  \u003c\/p\u003e\u003cp\u003eYou shall not: (1) share, resell, rent, assign, timeshare, distribute, or transfer all or part of the ebook or any rights granted hereunder to any other person; (2) duplicate the ebook, except for a single backup or archival copy; (3) remove any proprietary notices, labels, or marks from the ebook; (4) transfer or sublicense title to the ebook to any other party.  \u003c\/p\u003e\u003cp\u003e3. 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