Python Machine Learning

Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2

Raschka, Sebastian 저자(글)

Packt Publishing · 2019년 12월 09일

책 소개

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.

작가정보

저자(글) Raschka, Sebastian

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology.

목차

• Giving Computers the Ability to Learn from Data
  Training Simple Machine Learning Algorithms for Classification
  A Tour of Machine Learning Classifiers Using scikit-learn
  Building Good Training Datasets - Data Preprocessing
  Compressing Data via Dimensionality Reduction
  Learning Best Practices for Model Evaluation and Hyperparameter Tuning
  Combining Different Models for Ensemble Learning
  Applying Machine Learning to Sentiment Analysis
  Embedding a Machine Learning Model into a Web Application
  Predicting Continuous Target Variables with Regression Analysis
  Working with Unlabeled Data - Clustering Analysis
  Implementing a Multilayer Artificial Neural Network from Scratch
  Parallelizing Neural Network Training with TensorFlow
  Going Deeper - The Mechanics of TensorFlow
  Classifying Images with Deep Convolutional Neural Networks
  Modeling Sequential Data Using Recurrent Neural Networks
  Generative Adversarial Networks for Synthesizing New Data
  Reinforcement Learning for Decision Making in Complex Environments

기본정보

ISBN	9781789955750 ( 1789955750 )
발행(출시)일자	2019년 12월 09일
쪽수	772쪽
크기	191 * 235 * 39 mm / 1302 g
총권수	1권
언어	영어