Szczegóły ebooka

Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Second Edition

Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Second Edition

Sebastian Raschka, Vahid Mirjalili

Ebook
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.

Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.

If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
  • 1. Giving Computers the Ability to Learn from Data
  • 2. Training Machine Learning Algorithms the Ability to Learn from Data
  • 3. A Tour of Machine Learning Classifiers Using Scikit-Learn
  • 4. Building Good Training Sets – 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
  • 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
  • Tytuł: Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Second Edition
  • Autor: Sebastian Raschka, Vahid Mirjalili
  • Tytuł oryginału: Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Second Edition
  • ISBN: 9781787126022, 9781787126022
  • Data wydania: 2017-09-20
  • Format: Ebook
  • Identyfikator pozycji: e_15pi
  • Wydawca: Packt Publishing