Intro; Title Page; Copyright Page; Table of Contents; Introduction; About This Book; Foolish Assumptions; Icons Used in This Book; Beyond the Book; Where to Go from Here; Part 1 Discovering Deep Learning; Chapter 1 Introducing Deep Learning; Defining What Deep Learning Means; Starting from Artificial Intelligence; Considering the role of AI; Focusing on machine learning; Moving from machine learning to deep learning; Using Deep Learning in the Real World; Understanding the concept of learning; Performing deep learning tasks; Employing deep learning in applications
Considering the Deep Learning Programming EnvironmentOvercoming Deep Learning Hype; Discovering the start-up ecosystem; Knowing when not to use deep learning; Chapter 2 Introducing the Machine Learning Principles; Defining Machine Learning; Understanding how machine learning works; Understanding that it's pure math; Learning by different strategies; Training, validating, and testing data; Looking for generalization; Getting to know the limits of bias; Keeping model complexity in mind; Considering the Many Different Roads to Learning; Understanding there is no free lunch
Discovering the five main approachesDelving into some different approaches; Awaiting the next breakthrough; Pondering the True Uses of Machine Learning; Understanding machine learning benefits; Discovering machine learning limits; Chapter 3 Getting and Using Python; Working with Python in this Book; Obtaining Your Copy of Anaconda; Getting Continuum Analytics Anaconda; Installing Anaconda on Linux; Installing Anaconda on MacOS; Installing Anaconda on Windows; Downloading the Datasets and Example Code; Using Jupyter Notebook; Defining the code repository; Getting and using datasets
Creating the ApplicationUnderstanding cells; Adding documentation cells; Using other cell types; Understanding the Use of Indentation; Adding Comments; Understanding comments; Using comments to leave yourself reminders; Using comments to keep code from executing; Getting Help with the Python Language; Working in the Cloud; Using the Kaggle datasets and kernels; Using the Google Colaboratory; Chapter 4 Leveraging a Deep Learning Framework; Presenting Frameworks; Defining the differences; Explaining the popularity of frameworks; Defining the deep learning framework
Choosing a particular frameworkWorking with Low-End Frameworks; Caffe2; Chainer; PyTorch; MXNet; Microsoft Cognitive Toolkit/CNTK; Understanding TensorFlow; Grasping why TensorFlow is so good; Making TensorFlow easier by using TFLearn; Using Keras as the best simplifier; Getting your copy of TensorFlow and Keras; Fixing the C++ build tools error in Windows; Accessing your new environment in Notebook; Part 2 Considering Deep Learning Basics; Chapter 5 Reviewing Matrix Math and Optimization; Revealing the Math You Really Need; Working with data; Creating and operating with a matrix
