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Artificial Intelligence By Example

You're reading from   Artificial Intelligence By Example Acquire advanced AI, machine learning, and deep learning design skills

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839211539
Length 578 pages
Edition 2nd 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 (23) Chapters Close

Preface 1. Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning 2. Building a Reward Matrix – Designing Your Datasets FREE CHAPTER 3. Machine Intelligence – Evaluation Functions and Numerical Convergence 4. Optimizing Your Solutions with K-Means Clustering 5. How to Use Decision Trees to Enhance K-Means Clustering 6. Innovating AI with Google Translate 7. Optimizing Blockchains with Naive Bayes 8. Solving the XOR Problem with a Feedforward Neural Network 9. Abstract Image Classification with Convolutional Neural Networks (CNNs) 10. Conceptual Representation Learning 11. Combining Reinforcement Learning and Deep Learning 12. AI and the Internet of Things (IoT) 13. Visualizing Networks with TensorFlow 2.x and TensorBoard 14. Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA) 15. Setting Up a Cognitive NLP UI/CUI Chatbot 16. Improving the Emotional Intelligence Deficiencies of Chatbots 17. Genetic Algorithms in Hybrid Neural Networks 18. Neuromorphic Computing 19. Quantum Computing 20. Answers to the Questions 21. Other Books You May Enjoy
22. Index

Chapter 2 – Building a Reward Matrix – Designing Your Datasets

  1. Raw data can be the input to a neuron and transformed with weights. (Yes | No)

    The answer is yes if the data is in numerical format. If it is in a proper numerical format, the input can be multiplied by the weights and biases.

    If the data is not in a numerical format, then it requires a numerical encoding phase.

  2. Does a McCulloch-Pitts neuron require a threshold? (Yes | No)

    The answer is yes. Adding up weights does not mean much if you do not have something to measure the value. It took months of work for McCulloch and Pitt to put this together. At first, time was in the equation, just like it is in our brain. But then, like Joseph Fourier (1768-1830), they found cycles that repeated themselves—periods that did not require much more than that neuron.

    Warren McCulloch and Walter Pitts invented the first neuron and published a paper in 1943...

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