->

Machine Learning Algorithms in Depth (Final Release)

 

Machine Learning Algorithms in Depth
by Vadim Smolyakov

English | 2024 | ISBN: 1633439216 | 328 pages | True PDF | 26.57 MB


Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.
Fully understanding how machine learning algorithms function is essential for any serious ML engineer. 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 probabilistic 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

    Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods.

    About the book

    Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You'll especially appreciate author Vadim Smolyakov's clear interpretations of Bayesian algorithms for Monte Carlo and Markov models.

    What's inside
  • Monte Carlo stock price simulation
  • EM algorithm for hidden Markov models
  • Imbalanced learning, active learning, and ensemble learning
  • Bayesian optimization for hyperparameter tuning
  • Anomaly detection in time-series

    About the reader

    For 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.

    Table of Contents

    PART 1
    1 Machine learning algorithms
    2 Markov chain Monte Carlo
    3 Variational inference
    4 Software implementation
    PART 2
    5 Classification algorithms
    6 Regression algorithms
    7 Selected supervised learning algorithms
    PART 3
    8 Fundamental unsupervised learning algorithms
    9 Selected unsupervised learning algorithms
    PART 4
    10 Fundamental deep learning algorithms
    11 Advanced deep learning algorithms

  •  TO MAC USERS: If RAR password doesn't work, use this archive program: 

    RAR Expander 0.8.5 Beta 4  and extract password protected files without error.


     TO WIN USERS: If RAR password doesn't work, use this archive program: 

    Latest Winrar  and extract password protected files without error.


     speedzodiac   |  

    Information
    Members of Guests cannot leave comments.




    rss