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Deep Learning By Example

You're reading from   Deep Learning By Example A hands-on guide to implementing advanced machine learning algorithms and neural networks

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788399906
Length 450 pages
Edition 1st Edition
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Author (1):
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Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
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Table of Contents (18) Chapters Close

Preface 1. Data Science - A Birds' Eye View 2. Data Modeling in Action - The Titanic Example FREE CHAPTER 3. Feature Engineering and Model Complexity – The Titanic Example Revisited 4. Get Up and Running with TensorFlow 5. TensorFlow in Action - Some Basic Examples 6. Deep Feed-forward Neural Networks - Implementing Digit Classification 7. Introduction to Convolutional Neural Networks 8. Object Detection – CIFAR-10 Example 9. Object Detection – Transfer Learning with CNNs 10. Recurrent-Type Neural Networks - Language Modeling 11. Representation Learning - Implementing Word Embeddings 12. Neural Sentiment Analysis 13. Autoencoders – Feature Extraction and Denoising 14. Generative Adversarial Networks 15. Face Generation and Handling Missing Labels 16. Implementing Fish Recognition 17. Other Books You May Enjoy

Data size and industry needs

Data is the information base of our learning calculations; any uplifting and imaginative thoughts will be nothing with the absence of information. So in the event that you have a decent information science application with the right information, at that point you are ready to go.

Having the capacity to investigate and extricate an incentive from your information is obvious these days notwithstanding to the structure of your information, however since enormous information is turning into the watchword of the day then we require information science apparatuses and advancements that can scale with this immense measure of information in an unmistakable learning time. These days everything is producing information and having the capacity to adapt to it is a test. Huge organizations, for example, Google, Facebook, Microsoft, IBM, and so on, manufacture their own adaptable information science arrangements keeping in mind the end goal to deal with the tremendous amount of information being produced once a day by their clients.

TensorFlow, is a machine intelligence/data science platform that was released as an open source library on November 9, 2016 by Google. It is a scalable analytics platform that enables data scientists to build complex systems with a vast amount of data in visible time and it also enables them to use greedy learning methods that require lots of data to get a good performance.

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