{"product_id":"9781633439214-machine-learning-algorithms-in-depth","title":"Machine Learning Algorithms in Depth","description":"\u003cmeta content=\"text\/html; charset=utf-8\" http-equiv=\"Content-Type\"\u003e\u003cp\u003e\u003cspan\u003eMachine 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 will learn the fundamentals of Bayesian inference and deep learning.\u003cbr\u003eDevelop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. \u003cp\u003eFor intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eMachine Learning Algorithms in Depth\u003c\/strong\u003e dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today.\u003c\/p\u003e \u003cp\u003eWith a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning.\u003c\/p\u003e \u003cp\u003eYou will explore practical implementations of dozens of ML algorithms, including:\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 Optimisation 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 Minimisation 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\u003eEach algorithm is fully explored with both math and practical implementations so you can see how they work and put into action.\u003c\/p\u003e About the technology \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 trade-offs 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.\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Rarewaves","offers":[{"title":"Default Title","offer_id":55179836260726,"sku":"9781633439214","price":86.47,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0092\/7504\/8033\/files\/stand_32582312.jpg?v=1738242393","url":"https:\/\/www.rarewaves.com\/products\/9781633439214-machine-learning-algorithms-in-depth","provider":"Rarewaves.com","version":"1.0","type":"link"}