book

Deep Learning For Dummies

Name: Deep Learning For Dummies
ISBN: 9781119543046

by John Paul Mueller, Luca Massaron

May 2019

Intermediate to advanced

368 pages

9h 55m

English

For Dummies

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Cover
Introduction
About This BookFoolish AssumptionsIcons Used in This BookBeyond the BookWhere to Go from Here
Part 1: Discovering Deep Learning
Chapter 1: Introducing Deep Learning
Defining What Deep Learning MeansUsing Deep Learning in the Real WorldConsidering the Deep Learning Programming EnvironmentOvercoming Deep Learning Hype
Chapter 2: Introducing the Machine Learning Principles
Defining Machine LearningConsidering the Many Different Roads to LearningPondering the True Uses of Machine Learning
Chapter 3: Getting and Using Python
Working with Python in this BookObtaining Your Copy of AnacondaDownloading the Datasets and Example CodeCreating the ApplicationUnderstanding the Use of IndentationAdding CommentsGetting Help with the Python LanguageWorking in the Cloud
Chapter 4: Leveraging a Deep Learning Framework
Presenting FrameworksWorking with Low-End FrameworksUnderstanding TensorFlow
Part 2: Considering Deep Learning Basics
Chapter 5: Reviewing Matrix Math and Optimization
Revealing the Math You Really NeedUnderstanding Scalar, Vector, and Matrix OperationsInterpreting Learning as Optimization
Chapter 6: Laying Linear Regression Foundations
Combining VariablesMixing Variable TypesSwitching to ProbabilitiesGuessing the Right FeaturesLearning One Example at a Time

Chapter 7: Introducing Neural Networks
Discovering the Incredible PerceptronHitting Complexity with Neural NetworksStruggling with Overfitting
Chapter 8: Building a Basic Neural Network
Understanding Neural NetworksLooking Under the Hood of Neural Networks
Chapter 9: Moving to Deep Learning
Seeing Data EverywhereDiscovering the Benefits of Additional DataImproving Processing SpeedExplaining Deep Learning Differences from Other Forms of AIFinding Even Smarter Solutions
Chapter 10: Explaining Convolutional Neural Networks
Beginning the CNN Tour with Character RecognitionExplaining How Convolutions WorkDetecting Edges and Shapes from Images
Chapter 11: Introducing Recurrent Neural Networks
Introducing Recurrent NetworksExplaining Long Short-Term Memory
Part 3: Interacting with Deep Learning
Chapter 12: Performing Image Classification
Using Image Classification ChallengesDistinguishing Traffic Signs
Chapter 13: Learning Advanced CNNs
Distinguishing Classification TasksPerceiving Objects in Their SurroundingsOvercoming Adversarial Attacks on Deep Learning Applications
Chapter 14: Working on Language Processing
Processing LanguageMemorizing Sequences that MatterUsing AI for Sentiment Analysis
Chapter 15: Generating Music and Visual Art
Learning to Imitate Art and LifeMimicking an Artist
Chapter 16: Building Generative Adversarial Networks
Making Networks CompeteConsidering a Growing Field
Chapter 17: Playing with Deep Reinforcement Learning
Playing a Game with Neural NetworksExplaining Alpha-Go
Part 4: The Part of Tens
Chapter 18: Ten Applications that Require Deep Learning
Restoring Color to Black-and-White Videos and PicturesApproximating Person Poses in Real TimePerforming Real-Time Behavior AnalysisTranslating LanguagesEstimating Solar Savings PotentialBeating People at Computer GamesGenerating VoicesPredicting DemographicsCreating Art from Real-World PicturesForecasting Natural Catastrophes
Chapter 19: Ten Must-Have Deep Learning Tools
Compiling Math Expressions Using TheanoAugmenting TensorFlow Using KerasDynamically Computing Graphs with ChainerCreating a MATLAB-Like Environment with TorchPerforming Tasks Dynamically with PyTorchAccelerating Deep Learning Research Using CUDASupporting Business Needs with Deeplearning4jMining Data Using Neural DesignerTraining Algorithms Using Microsoft Cognitive Toolkit (CNTK)Exploiting Full GPU Capability Using MXNet
Chapter 20: Ten Types of Occupations that Use Deep Learning
Managing PeopleImproving MedicineDeveloping New DevicesProviding Customer SupportSeeing Data in New WaysPerforming Analysis FasterCreating a Better Work EnvironmentResearching Obscure or Detailed InformationDesigning BuildingsEnhancing Safety
Index
About the Authors
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End User License Agreement

Content preview from Deep Learning For Dummies

Chapter 12

Performing Image Classification

IN THIS CHAPTER

Recognizing the key contributions of image recognition challenges

Discovering the importance of image augmentation

Using the German Traffic Sign Benchmark dataset

Creating your own CNN capable of classifying traffic signs

Understanding how convolutional layers work, as shown in Chapter 10, is just a starting point. Theory can only explain how things work, but it can’t adequately describe the success of deep neural network solutions in the image-recognition field. The great part of this technology’s success, especially in AI applications, comes from the availability of suitable data to train and test image networks, their application to different problems thanks to transfer learning, and further sophistication of the technology that allows it to answer complex questions about image content.

In this chapter, you delve into the topic of object classification and detection challenges to discover their contribution in the foundation of the present deep learning renaissance. Competitions, such as those based on the ImageNet dataset, ...

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Deep Learning For Dummies

by John Paul Mueller, Luca Massaron

Performing Image Classification

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.