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Python Machine Learning - Third Edition

You're reading from  Python Machine Learning - Third Edition

Product type Book
Published in Dec 2019
Publisher Packt
ISBN-13 9781789955750
Pages 772 pages
Edition 3rd Edition
Languages
Concepts
Authors (2):
Sebastian Raschka Sebastian Raschka
Profile icon Sebastian Raschka
LinkedIn
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.
See other products by Sebastian Raschka
Vahid Mirjalili Vahid Mirjalili
Profile icon Vahid Mirjalili
LinkedIn
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.
See other products by Vahid Mirjalili
View More author details

Table of Contents (21) Chapters

Preface 1. Giving Computers the Ability to Learn from Data 2. Training Simple Machine Learning Algorithms for Classification 3. A Tour of Machine Learning Classifiers Using scikit-learn 4. Building Good Training Datasets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Implementing a Multilayer Artificial Neural Network from Scratch 13. Parallelizing Neural Network Training with TensorFlow 14. Going Deeper – The Mechanics of TensorFlow 15. Classifying Images with Deep Convolutional Neural Networks 16. Modeling Sequential Data Using Recurrent Neural Networks 17. Generative Adversarial Networks for Synthesizing New Data 18. Reinforcement Learning for Decision Making in Complex Environments 19. Other Books You May Enjoy 20. Index

Index

Symbols

1-gram model 280

5x2 cross-validation 219

7-Zip

URL 276

A

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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Python Machine Learning - Third Edition
Published in: Dec 2019 Publisher: Packt ISBN-13: 9781789955750
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Authors (2)

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Profile icon Sebastian Raschka
LinkedIn
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.
See other products by Sebastian Raschka
Profile icon Vahid Mirjalili
LinkedIn
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.
See other products by Vahid Mirjalili
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