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PYTHON MACHINE LEARNING MACHINE LEARNING AND DEEP LEARNING FROSTRATED WITH PYTHON SCIKIT LEARN KERAS THEANO AND TENSORFLOW 3rd edition by Raschka, Sebastian, Mirjalili, Vahid ISBN 1789955750 978-1789955750

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PYTHON MACHINE LEARNING MACHINE LEARNING AND DEEP LEARNING FROSTRATED WITH PYTHON SCIKIT LEARN KERAS THEANO AND TENSORFLOW 3rd edition by Raschka, Sebastian, Mirjalili, Vahid – Ebook PDF Instant Download/Delivery:  1789955750, 978-1789955750
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Product details:

ISBN 10: 1789955750
ISBN 13: 978-1789955750
Author: Raschka, Sebastian, Mirjalili, Vahid 

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you’ll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It’s also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you’re a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

PYTHON MACHINE LEARNING MACHINE LEARNING AND DEEP LEARNING FROSTRATED WITH PYTHON SCIKIT LEARN KERAS THEANO AND TENSORFLOW 3rd Table of contents:

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

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Tags: Raschka, Sebastian, Mirjalili, Vahid, PYTHON MACHINE, DEEP LEARNING