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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
Authors (2):
Sebastian Raschka Sebastian Raschka
Profile icon Sebastian Raschka
Vahid Mirjalili Vahid Mirjalili
Profile icon 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

Classifying handwritten digits

In the previous section, we covered a lot of the theory around NNs, which can be a little bit overwhelming if you are new to this topic. Before we continue with the discussion of the algorithm for learning the weights of the MLP model, backpropagation, let's take a short break from the theory and see an NN in action.

Additional resources on backpropagation

The NN theory can be quite complex; thus, it is recommended that you refer to two additional resources, which cover some of the concepts that we discuss in this chapter in more detail:

  • Chapter 6, Deep Feedforward Networks, Deep Learning, I. Goodfellow, Y. Bengio, and A. Courville, MIT Press, 2016 (Manuscripts freely accessible at http://www.deeplearningbook.org).
  • Pattern Recognition and Machine Learning, C. M. Bishop and others, Volume 1. Springer New York, 2006.
  • Lecture slides from the deep learning course at the University of Wisconsin–Madison...
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