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Artificial Intelligence By Example

You're reading from   Artificial Intelligence By Example Develop machine intelligence from scratch using real artificial intelligence use cases

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788990547
Length 490 pages
Edition 1st Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (19) Chapters Close

Preface 1. Become an Adaptive Thinker 2. Think like a Machine FREE CHAPTER 3. Apply Machine Thinking to a Human Problem 4. Become an Unconventional Innovator 5. Manage the Power of Machine Learning and Deep Learning 6. Don't Get Lost in Techniques – Focus on Optimizing Your Solutions 7. When and How to Use Artificial Intelligence 8. Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies 9. Getting Your Neurons to Work 10. Applying Biomimicking to Artificial Intelligence 11. Conceptual Representation Learning 12. Automated Planning and Scheduling 13. AI and the Internet of Things (IoT) 14. Optimizing Blockchains with AI 15. Cognitive NLP Chatbots 16. Improve the Emotional Intelligence Deficiencies of Chatbots 17. Quantum Computers That Think 18. Answers to the Questions

Summary

In any machine learning project, we should start by proving that classical programs using SQL queries will not suffice. Then, a baseline or prototype using a sample should prove that the model will work.

Using a cloud platform such as AWS SageMaker with S3 will save corporate investment resources in order to focus on the training job. This proves once again that the future of artificial intelligence experts relies upon their ability to imagine solutions and not to get lost in programming.

No matter what happens, the basic steps will always remain the same, which include proving that artificial intelligence is necessary, building a prototype model, dataset management, and hyperparameter and job optimization.

Amazon, Google, IBM, Microsoft, and others will be offering more platforms, frameworks, and resources for machine learning. These platforms will constantly evolve and...

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