File Size: 5334 KB
Print Length: 316 pages
Publisher: Blue Windmill Media (August 29, 2017)
Publication Date: August 29, 2017
There are over 200 images formatted for a Kindle fire Reader. If computer technology or Information technology is not your fiield, you will not discover the material useful and find it difficult to understand the ideas, statistical equations, math computations, diagrams, and explanations.
Just what is neural networks?
Neural networks have made a gigantic comeback in the final many years and you likely make use of them everyday without realizing it, but what exactly is a nerve organs network? What is it used for and how does it fit within the broader arena of machine learning?
Machine learning is the science of obtaining computers to act without being explicitly programmed, and it is our biggest step towards making synthetic intelligence a reality. When you like movies, think Ex Machina or Transcendence or I, Robot - but just the good parts! Machine learning is a field within computer technology that has gained incredible traction within the final few decades and it likely touches your life everyday.
A new neural network, also known as an artificial nerve organs network, is a type of machine learning algorithm that is inspired by the biological brain. That is one of many popular algorithms that is utilized within the world of machine learning, and its goal is to solve problems in a similar way to the human brain.
Neural networks are part of what’s called Strong Learning, which is a branch of machine learning that has proved valuable for solving difficult problems, such as recognizing things in images and vocabulary processing.
Neural networks take a different approach to solving problems than that of conventional computer programs. To be able to solve a problem, regular software uses an algorithmic approach, i. e. the pc follows a set of instructions so as to solve a problem. In distinction, neural networks approach problems in an exceedingly different way by wanting to mimic how neurons in the human brain work. Actually they learn by example rather than being programmed to perform a specific task. Technically, they are composed of a sizable quantity of highly connected with each other processing elements (nodes) that work in parallel to solve a specific problem, which is similar to how the human brain works.
A short concise definition is first provided for each term, then additional details that dive into the nitty-gritty, at the end of the guide. When you already have a general understanding, you might not get much out of this book. You don’t need to read front to back. Skip around to what you find the most helpful or is perking your interest.
There are many links all through the book to make learning easy. Simply click on them.
New terminology and concepts are progressively layered from chapter to section. This means that if you jump to section 4 without having read section 3, you could come across terminology that you do not understand. You can always jump back to clarify a topic or concept.
A guide for a very specific target party of practitioners and student.
11 October 2017, This is an excellent book which includes a fancy topic such as Neural Networks - I started reading this book with no familiarity with Neural networks, but after reading it, I can admit I do understand the principles of Neural Networks, a second or 3rd read and I can be a probationer/developer/researcher - yes this book is comprehensive on the principle, practicals, tools and techniques to get anyone began off on Neural Networks.
First of all let us understand the Potential audience - this book is a lot of math concepts, it will work even if you have a good backdrop in Math that is enough to understand the ideas and understand, but if you are a completely from a non-math background, this is not for you. I graduated about 22 years back and totally out of touch with theory, but I used to be able to understand it, so some background/concept is necessary, but if you act like you are really good in math - this book really will go deep into it.
The author does a best wishes in simplifying the ideas, example a partial type is beautifully explained as " enables you a measure how a single variable out of numerous impacts another single variable" and string rule is explained as " Discovering the error of the precise weight is an important aspect of training the networks"
Whenever I first browse the first few chapters of the book, it felt that was going nowhere - there were concepts around nodes, weights, error... some of which I used to be able to understand and some I really could not. So but it really all arrived together when I was on the practical example - a neural network which can read a image and determine if it is a chicken breast or a man. That explains a straightforward 64 -pixel image, each pixel contains a number which represents the color and based on the color - we should arrive at 0 for chicken breast and 1 for man. And how we can keep on adjusting weights until we arrive at the right answer and minimize error.
Essentially the image is reduced to just one quantity and that single quantity is derived by determining weights assigned to each pixel - to me personally this was poetic and achieved my NNN (Neural Network Nirvana) 33, 500 feet above ground when i read this book on my way back from on an international trip!
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