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Artificial Intelligence By Example
Artificial Intelligence By Example

Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases

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Artificial Intelligence By Example

Think like a Machine

The first chapter described a reinforcement learning algorithm through the Q action-value function used by DQN. The agent was a driver. You are at the heart of DeepMind's approach to AI.

DeepMind is no doubt one of the world leaders in applied artificial intelligence. Scientific, mathematical, and applications research drives its strategy.

DeepMind was founded in 2010, was acquired by Google in 2014, and is now part of Alphabet, a collection of companies that includes Google.

One of the focuses of DeepMind is on reinforcement learning. They came up with an innovate version of reinforcement learning called DQN and referring to deep neural networks using the Q function (Bellman's equation). A seminal article published in February 2015 in Nature (see the link at the end of the chapter) shows how DQN outperformed other artificial intelligence research...

Technical requirements

Designing datasets – where the dream stops and the hard work begins

As in the previous chapter, bear in mind that a real-life project goes through a three-dimensional method in some form or the other. First, it's important to just think and talk about the problem to solve without jumping onto a laptop. Once that is done, bear in mind that the foundation of machine learning and deep learning relies on mathematics. Finally, once the problem has been discussed and mathematically represented, it is time to develop the solution.

First, think of a problem in natural language. Then, make a mathematical description of a problem. Only then, start the software implementation.

Designing datasets in natural language meetings

...

Logistic activation functions and classifiers

Now that the value of each location of L={l1,l2,l3,l4,l5,l6} contains its availability in a vector, the locations can be sorted from the most available to least available location. From there, the reward matrix for the MDP process described in the first chapter can be built.

Overall architecture

At this point, the overall architecture contains two main components:

  • Chapter 1: Become an Adaptive Thinker: A reinforcement learning program based on the value-action Q function using a reward matrix that is yet to be calculated. The reward matrix was given in the first chapter, but in real life, you'll often have to build it from scratch. This could take weeks to obtain.
  • Chapter...

Summary

Using a McCulloch-Pitts neuron with a logistic activation function in a one-layer network to build a reward matrix for reinforcement learning shows how to build real-life applications with AI technology.

Processing real-life data often requires a generalization of a logistic sigmoid function through a softmax function, and a one-hot function applied to logits to encode the data.

This shows that machine learning functions are tools that must be understood to be able to use all or parts of them to solve a problem. With this practical approach to artificial intelligence, a whole world of projects awaits you.

You can already use these first two chapters to present powerful trajectory models such as Amazon warehouses and deliveries to your team or customers. Furthermore, Amazon, Google, Facebook, Netflix, and many others are growing their data centers as we speak. Each data...

Questions

  1. Was the concept of using an artificial neuron discovered in 1990? (Yes | No)
  2. Does a neuron require a threshold? (Yes | No)
  3. A logistic sigmoid activation function makes the sum of the weights larger. (Yes | No)
  4. A McCulloch-Pitts neuron sums the weights of its inputs. (Yes | No)
  5. A logistic sigmoid function is a log10 operation. (Yes | No)
  6. A logistic softmax is not necessary if a logistic sigmoid function is applied to a vector. (Yes | No)
  7. A probability is a value between -1 and 1. (Yes | No)

Further reading

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Key benefits

  • AI-based examples to guide you in designing and implementing machine intelligence
  • Develop your own method for future AI solutions
  • Acquire advanced AI, machine learning, and deep learning design skills

Description

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.

Who is this book for?

Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of artificial intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book.

What you will learn

  • Use adaptive thinking to solve real-life AI case studies
  • Rise beyond being a modern-day factory code worker
  • Acquire advanced AI, machine learning, and deep learning designing skills
  • Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology
  • Understand future AI solutions and adapt quickly to them
  • Develop out-of-the-box thinking to face any challenge the market presents
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Publication date : May 31, 2018
Length: 490 pages
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Language : English
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Table of Contents

18 Chapters
Become an Adaptive Thinker Chevron down icon Chevron up icon
Think like a Machine Chevron down icon Chevron up icon
Apply Machine Thinking to a Human Problem Chevron down icon Chevron up icon
Become an Unconventional Innovator Chevron down icon Chevron up icon
Manage the Power of Machine Learning and Deep Learning Chevron down icon Chevron up icon
Don't Get Lost in Techniques – Focus on Optimizing Your Solutions Chevron down icon Chevron up icon
When and How to Use Artificial Intelligence Chevron down icon Chevron up icon
Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies Chevron down icon Chevron up icon
Getting Your Neurons to Work Chevron down icon Chevron up icon
Applying Biomimicking to Artificial Intelligence Chevron down icon Chevron up icon
Conceptual Representation Learning Chevron down icon Chevron up icon
Automated Planning and Scheduling Chevron down icon Chevron up icon
AI and the Internet of Things (IoT) Chevron down icon Chevron up icon
Optimizing Blockchains with AI Chevron down icon Chevron up icon
Cognitive NLP Chatbots Chevron down icon Chevron up icon
Improve the Emotional Intelligence Deficiencies of Chatbots Chevron down icon Chevron up icon
Quantum Computers That Think Chevron down icon Chevron up icon
Answers to the Questions Chevron down icon Chevron up icon

Customer reviews

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Zeshan Aug 01, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was looking for a "one stop" resource for AI. Once you start looking for AI learning online, you're overwhelmed with non-stop content, some of it is good and a lot of it isn't. Denis in this book has managed to strike the right balance of separating hype from reality. With no nonsense language, he builds up a picture of AI and its uses together with practical examples that you can set up in Virtual Machines. If you're looking for a straightforward learning resource for AI, this is it! Highly recommended!
Amazon Verified review Amazon
Prometheus Unbound Aug 10, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
AI by Example is a very readable book. Though he does not cover all AI topics (which is not it's intent), the author does a very good job at guiding the reader through "how to think about AI" by, of course, using examples. For each subject area, the author explains the conceptual, mathematical and implementation viewpoints in an easy to understand manner. As such, the author acts as a teacher who communicates through readable and accessible discourse. I would say the book is a good book for beginners who want to get started in understanding and coding some common AI algorithms, and early career AI engineers who want to sharpen how they think about AI in abstract and practical ways.
Amazon Verified review Amazon
Advait Jul 27, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I am a newbie artificial intelligence concepts yet I found this book extremely immersive,interactive and enjoyable to learn from.What makes this book stand out from other is the range of practical examples given with the complete code along with it.I feel it is totally worth spending your time learning from this book
Amazon Verified review Amazon
Juan Astudillo Aug 21, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
When i have been consulted by an Artificial Intelligence Book, i always think in this book . It's Complete from begin to end . The Author take you to overcome yourself transforming you from an Adaptive Thinker until to understand how a Quantum Copumter works. Amazing Book. Highgly recommended.
Amazon Verified review Amazon
Adnan Oct 31, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Fancy cover page. Yet, the rest of the book is printed in low quality papers and not colored figures..!!!
Amazon Verified review Amazon
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