|
|
|
|
LEADER |
00000cam a2200000Mi 4500 |
001 |
on1039688637 |
003 |
OCoLC |
005 |
20240809213013.0 |
006 |
m o d |
007 |
cr |n|---||||| |
008 |
180609s2018 enk o 000 0 eng d |
040 |
|
|
|a EBLCP
|b eng
|e pn
|c EBLCP
|d MERUC
|d NLE
|d UWW
|d OCLCQ
|d UKMGB
|d OCLCO
|d LVT
|d IDB
|d UKAHL
|d OCLCQ
|d OCLCO
|d K6U
|d OCLCO
|d OCLCQ
|d OCLCO
|d OCLCL
|d SXB
|d OCLCQ
|
015 |
|
|
|a GBB8J1051
|2 bnb
|
016 |
7 |
|
|a 018897099
|2 Uk
|
020 |
|
|
|a 9781788625906
|
020 |
|
|
|a 1788625900
|
020 |
|
|
|z 9781788621113
|
035 |
|
|
|a (OCoLC)1039688637
|
037 |
|
|
|a 9781788625906
|b Packt Publishing
|
050 |
|
4 |
|a Q325.5
|
049 |
|
|
|a HCDD
|
100 |
1 |
|
|a Bonaccorso c/o Quandoo, Giuseppe.
|
245 |
1 |
0 |
|a Mastering Machine Learning Algorithms :
|b Expert techniques to implement popular machine learning algorithms and fine-tune your models.
|
260 |
|
|
|a Birmingham :
|b Packt Publishing,
|c 2018.
|
300 |
|
|
|a 1 online resource (567 pages)
|
336 |
|
|
|a text
|b txt
|2 rdacontent
|
337 |
|
|
|a computer
|b c
|2 rdamedia
|
338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
588 |
0 |
|
|a Print version record.
|
505 |
0 |
|
|a Cover; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Model Fundamentals; Models and data; Zero-centering and whitening; Training and validation sets; Cross-validation; Features of a machine learning model; Capacity of a model; Vapnik-Chervonenkis capacity; Bias of an estimator; Underfitting; Variance of an estimator; Overfitting; The Cramér-Rao bound; Loss and cost functions; Examples of cost functions; Mean squared error; Huber cost function; Hinge cost function; Categorical cross-entropy; Regularization; Ridge; Lasso.
|
505 |
8 |
|
|a ElasticNetEarly stopping; Summary; Chapter 2: Introduction to Semi-Supervised Learning; Semi-supervised scenario; Transductive learning; Inductive learning; Semi-supervised assumptions; Smoothness assumption; Cluster assumption; Manifold assumption; Generative Gaussian mixtures; Example of a generative Gaussian mixture; Weighted log-likelihood; Contrastive pessimistic likelihood estimation; Example of contrastive pessimistic likelihood estimation; Semi-supervised Support Vector Machines (S3VM); Example of S3VM; Transductive Support Vector Machines (TSVM); Example of TSVM; Summary.
|
505 |
8 |
|
|a Chapter 3: Graph-Based Semi-Supervised LearningLabel propagation; Example of label propagation; Label propagation in Scikit-Learn; Label spreading; Example of label spreading; Label propagation based on Markov random walks; Example of label propagation based on Markov random walks; Manifold learning; Isomap; Example of Isomap; Locally linear embedding; Example of locally linear embedding; Laplacian Spectral Embedding; Example of Laplacian Spectral Embedding; t-SNE; Example of t-distributed stochastic neighbor embedding ; Summary; Chapter 4: Bayesian Networks and Hidden Markov Models.
|
505 |
8 |
|
|a Conditional probabilities and Bayes' theoremBayesian networks; Sampling from a Bayesian network; Direct sampling; Example of direct sampling; A gentle introduction to Markov chains; Gibbs sampling; Metropolis-Hastings sampling; Example of Metropolis-Hastings sampling; Sampling example using PyMC3; Hidden Markov Models (HMMs); Forward-backward algorithm; Forward phase; Backward phase; HMM parameter estimation; Example of HMM training with hmmlearn; Viterbi algorithm; Finding the most likely hidden state sequence with hmmlearn; Summary; Chapter 5: EM Algorithm and Applications.
|
505 |
8 |
|
|a MLE and MAP learningEM algorithm; An example of parameter estimation; Gaussian mixture; An example of Gaussian Mixtures using Scikit-Learn; Factor analysis; An example of factor analysis with Scikit-Learn; Principal Component Analysis; An example of PCA with Scikit-Learn; Independent component analysis; An example of FastICA with Scikit-Learn; Addendum to HMMs; Summary; Chapter 6: Hebbian Learning and Self-Organizing Maps; Hebb's rule; Analysis of the covariance rule; Example of covariance rule application; Weight vector stabilization and Oja's rule; Sanger's network.
|
500 |
|
|
|a Example of Sanger's network.
|
520 |
|
|
|a This book is your guide to quickly get to grips with the most widely used machine learning algorithms. As a data science professional, this book will help you design and train better machine learning models to solve a variety of complex problems, and make the machine learn your requirements.
|
650 |
|
0 |
|a Machine learning.
|
650 |
|
0 |
|a Computer algorithms.
|
650 |
|
7 |
|a algorithms.
|2 aat
|
650 |
|
7 |
|a Mathematical theory of computation.
|2 bicssc
|
650 |
|
7 |
|a Artificial intelligence.
|2 bicssc
|
650 |
|
7 |
|a Machine learning.
|2 bicssc
|
650 |
|
7 |
|a Information architecture.
|2 bicssc
|
650 |
|
7 |
|a Database design & theory.
|2 bicssc
|
650 |
|
7 |
|a Computers
|x Intelligence (AI) & Semantics.
|2 bisacsh
|
650 |
|
7 |
|a Computers
|x Machine Theory.
|2 bisacsh
|
650 |
|
7 |
|a Computers
|x Data Modeling & Design.
|2 bisacsh
|
650 |
|
7 |
|a Computer algorithms
|2 fast
|
650 |
|
7 |
|a Machine learning
|2 fast
|
776 |
0 |
8 |
|i Print version:
|a Bonaccorso c/o Quandoo, Giuseppe.
|t Mastering Machine Learning Algorithms : Expert techniques to implement popular machine learning algorithms and fine-tune your models.
|d Birmingham : Packt Publishing, ©2018
|
856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=5405679
|y Click for online access
|
903 |
|
|
|a EBC-AC
|
994 |
|
|
|a 92
|b HCD
|