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Deep Learning for Dummies By John Paul Mueller, Luca Massaron PDF

Download Book : Deep Learning for Dummies By John Paul Mueller, Luca Massaron PDF




Informations about the book:


TitleDeep Learning for Dummies

Author
Paul Mueller, Luca Massaron 

Size: 11.5 MB

Format: PDF

Year: 2019

Pages: 371


Book Contents:


Introduction 
Part 1: Discovering Deep Learning  
CHAPTER 1: Introducing Deep Learning  
CHAPTER 2: Introducing the Machine Learning Principles  
CHAPTER 3: Getting and Using Python  
CHAPTER 4: Leveraging a Deep Learning Framework  
Part 2: Considering Deep Learning Basics  
CHAPTER 5: Reviewing Matrix Math and Optimization  
CHAPTER 6: Laying Linear Regression Foundations  
CHAPTER 7: Introducing Neural Networks 
CHAPTER 8: Building a Basic Neural Network 
CHAPTER 9: Moving to Deep Learning  
CHAPTER 10: Explaining Convolutional Neural Networks 
CHAPTER 11: Introducing Recurrent Neural Networks  
Part 3: Interacting with Deep Learning 
CHAPTER 12: Performing Image Classification 
CHAPTER 13: Learning Advanced CNNs  
CHAPTER 14: Working on Language Processing  
CHAPTER 15: Generating Music and Visual Art  
CHAPTER 16: Building Generative Adversarial Networks  
CHAPTER 17: Playing with Deep Reinforcement Learning  
Part 4: The Part of Tens   
CHAPTER 18: Ten Applications that Require Deep Learning  
CHAPTER 19: Ten Must-Have Deep Learning Tools  
CHAPTER 20: Ten Types of Occupations that Use Deep Learning  


Description:


When you work through Deep Learning For Dummies, you gain access to a lot of
example code that will run on a standard Mac, Linux, or Windows system. You can
also run the code online using something like Google Colab. (We provide pointers
on how to get the information you need to do this.) Special equipment, such as a
GPU, will make the examples run faster. However, the point of this book is that
you can create deep learning code no matter what sort of machine you have as
long as you’re willing to wait for some of it to complete. (We tell you which examples take a long time to run.)
The first part of this book gives you some starter information so that you don’t get
completely lost before you start. You discover how to install the various products
you need and gain an understanding of some essential math. The beginning
examples are more along the lines of standard regression and machine learning,
but you need this basis to gain a full appreciation of just what deep learning can
do for you.
After you get past these initial bits of information, you start to do some pretty
amazing things. For example, you discover how to generate your own art and perform other tasks that you might have assumed to require many of coding and
some special hardware to accomplish. By the end of the book, you’ll be amazed by
what you can do, even if you don’t have an advanced machine learning or deep
learning degree.


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