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Hands-On Artificial Intelligence for Beginners

Hands-On Artificial Intelligence for Beginners

By : Dindi, Patrick D. Smith
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Hands-On Artificial Intelligence for Beginners

Hands-On Artificial Intelligence for Beginners

By: Dindi, Patrick D. Smith

Overview of this book

Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications.
Table of Contents (15 chapters)
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Constructing a basic agent

The simplest way to construct an artificial assistant with TensorFlow is to use a sequence-to-sequence (Seq2Seq) model, which we learned in the chapter on RNNs.

While originally developed for neural machine translation, we can adjust this model to act as an intelligent chatbot for our own purposes. We'll create the brain behind our assistant as a Python class called IntelligentAssistant. Then, we'll create the training and chatting functions for our assistant:

  1. First, let's start with our standard imports and initialize our variables. Take special note of the mask variable here; masks are placeholders that allow us to handle variable-length inputs in our network:
import numpy as np
import tensorflow as tf

class IntelligentAssistant:
''' The "Brain" behind our assistant '''

def __init__(self, forwardPass...
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Hands-On Artificial Intelligence for Beginners
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