Machine Learning Algorithms in Depth (MEAP V04)

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.



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