PirateBay Download | smolyakov v machine learning algorithms depth meap v7 2023

smolyakov v machine learning algorithms depth meap v7 2023

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Uploaded On:: March 19, 2023, 9:27 a.m.
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Textbook in PDF format Develop a mathematical intuition for how Machine Learning (ML) algorithms work so you can improve model performance and effectively troubleshoot complex ML problems. 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. PART 1: INTRODUCING ML ALGORITHMS 1 Machine Learning Algorithms 2 Markov Chain Monte Carlo 3 Variational Inference 4 Software Implementation PART 2: SUPERVISED LEARNING 5 Classification Algorithms 6 Regression Algorithms 7 Selected Supervised Learning Algorithms PART 3: UNSUPERVISED LEARNING 8 Fundamental Unsupervised Learning Algorithms 9 Selected Unsupervised Learning Algorithms PART 4: DEEP LEARNING 10 Fundamental Deep Learning Algorithms 11 Advanced Deep Learning Algorithms APPENDIXES

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Smolyakov V. Machine Learning Algorithms in Depth (MEAP v7) 2023.pdf 48.6 MB

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smolyakov v machine learning algorithms depth meap v7 2023

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