Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Przednia okładka
"O'Reilly Media, Inc.", 5 wrz 2019 - 856

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use Scikit-Learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets
 

Wybrane strony

Spis treści

Part II Neural Networks and Deep Learning
277
Appendix A Exercise Solutions
719
Appendix B Machine Learning Project Checklist
755
Appendix C SVM Dual Problem
761
Appendix D Autodiff
765
Appendix E Other Popular ANN Architectures
773
Appendix F Special Data Structures
783
Appendix G TensorFlow Graphs
791
Index
801
About the Author
820
Prawa autorskie

Inne wydania - Wyświetl wszystko

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts ...
Aurélien Géron
Ograniczony podgląd - 2019
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts ...
Aurélien Géron
Podgląd niedostępny - 2019

Kluczowe wyrazy i wyrażenia

activation function architecture arXiv autoencoder backpropagation batch called Chapter classifier compute containing convolutional layer cost function create dataset decision boundary Decision Tree decoder default Dense layer dimensionality reduction dropout dtype=float32 embedding encoder environment Equation error evaluate example feature maps Figure Google Gradient Descent graph hidden layers hyperparameter images implement input Iris virginica iteration Keras keras.layers keras.models.Sequential kernel labels learning algorithm learning rate linear Logistic Regression look loss Machine Learning matrix method metrics mini-batch MNIST multiple neural network neurons NumPy optimization output layer overfitting parameters performance pixels pooling layer predictions preprocessing pretrained probability problem Python Q-Learning Q-Value Random Forest Regression regular Reinforcement Learning ReLU rewards sample Scikit-Learn sequence softmax step task technique tensor TensorFlow test set TF Function tf.keras tf.Tensor tion training data training instances training set unsupervised unsupervised learning update validation set vector weights word y_train

Informacje o autorze (2019)

Aurélien Géron is a machine learning consultant and trainer. A former Googler, he led YouTube's video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst (a leading Wireless ISP in France) from 2002 to 2012, and a founder and CTO of two consulting firms -- Polyconseil (telecom, media and strategy) and Kiwisoft (machine learning and data privacy).

Informacje bibliograficzne

TytułHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
AutorAurélien Géron
Wydanie2
Wydawca"O'Reilly Media, Inc.", 2019
ISBN1492032611, 9781492032618
Liczba stron856
  
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