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  • Neural Networks

  • Deep Learning and Machine Learning Outlined
  • By: Quinn Spencer
  • Narrated by: Eric Boozer
  • Length: 1 hr and 5 mins
  • Unabridged Audiobook

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Publisher's Summary

Would you achieve more if you could envision your success?  

A neural network is a computing system made up of a number of simple, highly interconnected processing elements, which transfer information by their dynamic state response to external inputs. All of this sounds fancy, but what does it mean for computer intelligence or the future? 

In this book, you will find answers to many practical and theoretical questions related to neural networks, from insights about nodes and hidden layers to error spaces, network analyses, and computing influences. Topics will be discussed include:

  • What the definition of neural networks encompasses and what all the elements pertaining to them mean.
  • The main advantages of neural networks and how to leverage and apply them.
  • Limitations to neural networks.
  • How neural networks differ from conventional computing systems.
  • Neural network applications for medical diagnostics, smart computers, artificial intelligence, and forex or stock trading.
  • Troubleshooting tips for when neural networks stop functioning.

If you are even in the least interested in computer technology, artificial intelligence, or what the technological future will bring, you need to listen to this book and get a better understanding of neural networks and their many applications. This book will bring you to the core of how they function and what you can do with them.
 

©2018 Quinn Spencer (P)2019 Quinn Spencer

What listeners say about Neural Networks

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  • Dan
  • 12-04-2019

Why didn't I see this one any sooner?

I mean, there are some of those books out there about the same topic but this one stood out. I really liked the narrator and I might look for similar books from that author and narrator.

4 people found this helpful

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  • Leo
  • 12-04-2019

This is big

This stuff is the future. I mean, have you ever followed things about deep learning and deep machine artificial intelligence? This is a very insightful book.

3 people found this helpful

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  • Ewan
  • 12-04-2019

Just what I was looking for

This book has everything that relates to the topic. I actually liked the cover. So that's why I bought it, but when I listened to it, I realized the author had really put some thought into it.

2 people found this helpful

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  • Anonymous User
  • 07-01-2020

Great stuff

I liked this book a lot. I didn't know much about ai, or neural networks, or anything computer related. The picture on the cover gives me more of the impression of the human brain, but I guess it's all related to intelligence, real intelligence, and artificial ones. Good narrator too Glad I found it.

1 person found this helpful

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  • Leslie G. Garza
  • 16-04-2019

This book offers nothing one couldn't Google

Not bad if you have never heard of neural networks and are simultaneously well versed in statistics. The author offers no real insights into implementation and the few examples provided are heavily weighted toward medical applications. The only other types of examples are financial in nature and mostly superficial.

1 person found this helpful

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  • Marek
  • 08-05-2019

Poorly written / information focuses on trading

terrible narration, mispronouncing words like paradigm. story is poor. information is not suited for an audiobook. this book is too high level to have value but too low level to listen in audio format. it also talks about the most obvious things rather than anything novel.

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  • Jacob
  • 10-03-2020

performance is less than impressive

struggled to finish it. because of the appaling performance. I'm not sure if the problem is with the apparent lack of review of the spoken text, or not reading the text correctly before speaking it.
some details below.

Not lousy compression but lossy compression.

Not John Hopskins but Johns Hopkins.

Not para dim, but paradigm.

Local specular edges instead of local spectacular edges. 

Local specular areas  instead of local spectacular areas 

Not expotentials but exponentials

Not co-zeen but cosine

Not implicity but implicitly

Not arbitrarily number, of arbitrary number 

Not patterns that remain "invincible" but "invisible",

Not "the morality of due to the infection" but "the mortality due to the infection"

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  • Noah
  • 12-04-2019

Definitely enjoyed it

This is a very good book. I highly recommend it. Neural networks is a spot-on modern topic many have come across, and it's applicable to this millennial era.

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