Details

Deep Learning For Dummies


Deep Learning For Dummies


1. Aufl.

von: John Paul Mueller, Luca Massaron

22,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 17.04.2019
ISBN/EAN: 9781119543039
Sprache: englisch
Anzahl Seiten: 368

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.

Beschreibungen

<p><b>Take a deep dive into deep learning</b><b> </b></p> <p>Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. <i>Deep Learning for Dummies</i> gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it.    </p> <p>In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types.</p> <ul> <li>Includes sample code</li> <li>Provides real-world examples within the approachable text</li> <li>Offers hands-on activities to make learning easier</li> <li>Shows you how to use Deep Learning more effectively with the right tools</li> </ul> <p>This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.  </p>
<p>Introduction 1</p> <p><b>Part 1: Discovering Deep Learning 7</b></p> <p>Chapter 1: Introducing Deep Learning 9</p> <p>Chapter 2: Introducing the Machine Learning Principles 25</p> <p>Chapter 3: Getting and Using Python 45</p> <p>Chapter 4: Leveraging a Deep Learning Framework 73</p> <p><b>Part 2: Considering Deep Learning Basics 91</b></p> <p>Chapter 5: Reviewing Matrix Math and Optimization 93</p> <p>Chapter 6: Laying Linear Regression Foundations 111</p> <p>Chapter 7: Introducing Neural Networks 131</p> <p>Chapter 8: Building a Basic Neural Network 149</p> <p>Chapter 9: Moving to Deep Learning 163</p> <p>Chapter 10: Explaining Convolutional Neural Networks 179</p> <p>Chapter 11: Introducing Recurrent Neural Networks 201</p> <p><b>Part 3: Interacting with Deep Learning 215</b></p> <p>Chapter 12: Performing Image Classification 217</p> <p>Chapter 13: Learning Advanced CNNs 233</p> <p>Chapter 14: Working on Language Processing 251</p> <p>Chapter 15: Generating Music and Visual Art 269</p> <p>Chapter 16: Building Generative Adversarial Networks 279</p> <p>Chapter 17: Playing with Deep Reinforcement Learning 293</p> <p><b>Part 4: The Part of Tens 307</b></p> <p>Chapter 18: Ten Applications that Require Deep Learning 309</p> <p>Chapter 19: Ten Must-Have Deep Learning Tools 317</p> <p>Chapter 20: Ten Types of Occupations that Use Deep Learning 327</p> <p>Index 335</p>
<p><b>John Paul Mueller</b> is the author of over 100 books including <i>AI for Dummies, Python for Data Science for Dummies, Machine Learning for Dummies,</i> and <i> Algorithms for Dummies</i>. <b>Luca Massaron</b> is a data scientist who interprets big data and transforms it into smart data by means of the simplest and most effective data mining and machine learning techniques. He is a Google Developer Expert (GDE) in machine learning.
<ul> <li>Learn how deep learning is an essential technology</li> <li>Experiment with deep learning in a Python environment</li> <li>See examples of major deep learning application types</li> </ul> <p><b>Dive deep into deep learning </b> <p>Deep learning provides the means for discerning patterns in the data that drive online business, medicine, research, social media outlets, and many elements of daily life. This book gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it.??In no time, you'll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. <p><b>Inside...</b> <ul> <li>Create examples using TensorFlow and Keras</li> <li>Benefit from hands-on activities to make learning easier</li> <li>Use deep learning more effectively with the right tools</li> <li>Make sense of complex algorithms</li> </ul>

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