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You're reading from Python Machine Learning - Third Edition

Product typeBook

Published inDec 2019

Reading LevelExpert

PublisherPackt

ISBN-139781789955750

Edition3rd Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Machine Learning

Authors (2):

Sebastian Raschka

Vahid Mirjalili

View More author details

Index

Symbols

1-gram model 280

5x2 cross-validation 219

7-Zip

URL 276

accuracy

versus classification error 59

accuracy 222

action-value function

about 712

greedy policy, computing from 720

action-value function estimation

with Monte Carlo (MC) 719

activation functions

logistic function 492, 493

Rectified linear unit (ReLU) 497, 498

reference link 499

selecting, for multilayer neural networks 491

softmax function 494

activation functions, selecting via tf.keras.activations

reference link 516

activations

computing, in RNN 603, 604, 605

AdaBoost

applying, scikit-learn used 269, 270, 271

AdaBoost recognition

about 264

Adaline

about 56

implementing, in Python 42, 43, 44, 45

Adaline implementation

converting, into algorithm for logistic regression 70, 71, 73

adaptive boosting

weak learner, leveraging via 264, 265

Adaptive Boosting (AdaBoost)

about 264

ADAptive...

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Authors (2)

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more about Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Read more about Vahid Mirjalili

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