02598nam a22002777a 4500003000400000005001700004008004100021020003000062040005900092100002500151245007400176260006800250300005700318336002100375337002500396338002300421504004800444505089300492520070101385521001002086541007502096546002002171650003902191650004002230650005002270OSt20260928103343.0260928b        |||||||| |||| 00| 0 eng d  a9789819814091 [paperback]  aUniversity of Cebu-BaniladcUniversity of Cebu-Banilad  aLiu, G. R.,eauthor.  aMachine learning with python :btheory and applications /cG. R. Liu.  aSingapore :bWorld Scientific Publishing Co. Pte. Ltd.,cc2023.  axxii, 670 pages :bcolored illustrations ;c24.4 cm.  2rdacontentatext  2rdamediaaunmediated  2rdacarrieravolume  aIncludes bibliographical references, index.  aContents: About the author — 1. Introduction — 2. Basics of python — 3. Basic mathematical computations — 4. Statistics and probability-based learning model — 5. Prediction function and universal prediction theory — 6. The perceptron and SVM — 7. Activation functions and universal approximation theory — 8. Automatic differentiation and autograd — 8. Solution existence theory and optimization techniques — 10 Loss function for regression — 11 Loss functions and models for classification — 12. Multiclass classification — 13 Multilayer perceptron (MLP) for regression and classification — 14 Overfitting and regularization — 15. Convulutional neural network (CNN) for classification and object detection — 16. Recurrent neural network (RNN) and sequence feature models — 17 Unsupervised learning techniques — 18 Reinforcement learning (RL) — Index.  a"Machine Learning (ML) has become a very important area of research widely used in various industries.

This compendium introduces the basic concepts, fundamental theories, essential computational techniques, codes, and applications related to ML models. With a strong foundation, one can comfortably learn related topics, methods, and algorithms. Most importantly, readers with strong fundamentals can even develop innovative and more effective machine models for his/her problems. The book is written to achieve this goal.

The useful reference text benefits professionals, academics, researchers, graduate and undergraduate students in AI, ML and neural networks." —Provided by the publisher  aAdult  aPurchasedxOrtega, EricyCollege of Computer StudieszComputer Science  aText in English  2Machine learningxData processing.  aPython (Computer program language).  aArtificial intelligencexMathematical models.