Book Image

Artificial Intelligence By Example

By : Denis Rothman
Book Image

Artificial Intelligence By Example

By: Denis Rothman

Overview of this book

Artificial intelligence has the potential to replicate humans in every field. Artificial Intelligence By Example serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, you will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision.
Table of Contents (19 chapters)

Chapter 16 – Improve the Emotional Intelligence Deficiencies of Chatbots

1. Restricted Boltzmann Machines are based on directed graphs. (Yes | No)

No. RBM graphs are undirected, unsupervised, and memoryless, and the decision making is based on random calculations.

2. The hidden units of an RBM are generally connected to each other. (Yes | No)

No. The hidden units of an RBM are not generally connected to each other.

3. Random sampling is not used in an RBM. (Yes | No)

No. False. Gibbs random sampling is frequently applied to RBMs.

4. Is there a method to prevent gradients from vanishing in an RNN? (Yes | No)

Yes. When the gradient gets "stuck" around 0, for example, a ReLU function can solve this problem. It will force negative values to become 0 (or a fixed value in a leaky ReLU), and the positive values will not change.

5. LSTM cells never forget. (Yes | No)

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