Download PDF Make Your Own Neural Network: An In-depth Visual Introduction For Beginners, by Michael Taylor

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Make Your Own Neural Network: An In-depth Visual Introduction For Beginners, by Michael Taylor

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Product details

Paperback: 248 pages

Publisher: Independently published (October 4, 2017)

Language: English

ISBN-10: 1549869132

ISBN-13: 978-1549869136

Product Dimensions:

6 x 0.6 x 9 inches

Shipping Weight: 12.8 ounces (View shipping rates and policies)

Average Customer Review:

4.0 out of 5 stars

76 customer reviews

Amazon Best Sellers Rank:

#49,398 in Books (See Top 100 in Books)

This is an excellent book which covers a complex topic such as Neural Networks - I started reading this book with no knowledge of Neural networks, but after reading it, I can say that 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 theory, practicals, tools and processes to get anyone started off on Neural Networks.First of all let us understand the Target Audience - this book is a lot of math, it will work even if you have a good background in Math that is enough to understand the concepts and understand, but if you are a totally 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 was able to understand it, so some background/concept is necessary, but if you are really good in math - this book really goes deep into it.The author does a great job in simplifying the concepts, example a partial derivative is beautifully explained as "enables you a measure how a single variable out of many impacts another single variable" and chain rule is explained as "Discovering the error of the specific weight is an important aspect of training the networks"When I first read the first few chapters of the book, it felt that this was going nowhere - there were concepts around nodes, weights, error ... some of which I was able to understand and some I could not. So but it really all came together when I was on the practical example - a neural network which can read a image and determine if it is a chicken or a man. It explains a simple 64 pixel image, each pixel contains a number which represents the color and based on the color - we should arrive at 0 for chicken 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 a single number and that single number is derived by assigning weights assigned to each pixel - to me this was poetic and achieved my NNN (Neural Network Nirvana) 33,000 feet above ground while I read this book on my way back from on an international trip !

You will understand what a neural network is, how it works, and how to create one.First, a caveat: “For Beginners” does mean to beginners to the topic of neural networks, not to all topics per se. Required skills include a decent grounding in algebra, statistics, and calculus. My experience found the algebra foundational, the statistics very friendly (I’ve used statistics a lot in some of my hobbies), and the calculus somewhat daunting and alien, having not touched the subject since my schooldays.However, fear not, as Taylor does link to (free) Khan academy courses for any of those fields, should you need a refresher and/or primer. Myself, I found it more convenient to dig out an old Open University maths textbook for this purpose, but your mileage may vary.Towards the latter part of the book, Taylor fulfils his promise of walking us through creating our first neural network using Python. For those not familiar with this programming language, again, resources are linked, though in this case your needs would be more grounded in the peripheral tools, since the language itself he explains, at least insofar as everything used in this project, term by term, line by line. Can’t go wrong. Frankly, I found it more elucidating than my first foray into Python some years back, and that was from a purpose-built “Learn Python” app.Right at the end are a number of appendices including two glossaries of terms; if you are (or suspect you may be) hazy over terms, it couldn’t hurt to perhaps read those before the rest of the book, as while Taylor explains many things as he goes, there are some things that I didn’t grasp as quickly as I would have had I read the glossaries first.All in all, fantastic book, of great value to those such as myself who try to maintain a conversant hobby interest in such matters, without having the time or resources to devote to a more comprehensive study — or indeed, for those simply looking for an “in” to a topic that can be made quite opaque by the tendency of those who write about it to assume more knowledge of the field than we have. So yes, this on the contrary is a work of great value, and I look forward to enjoying more from this author.

This book is described as an in-depth introduction to neural networks which defines neural networks, describes how they work, and explains how to build one. While it certainly covers all these topics, I found this book to be very difficult overall. I do not have a background in Python or higher level math, and while I could grasp the basic concepts, I was honestly very lost for a good portion of the book.At the same time, I did appreciate the author’s attempts to make neural networks understandable to a lay audience. Neural networks are becoming more and more relevant and it is definitely worthwhile to have some grasp of what they are and how they work. I think this book is best for people who have a background in a programming language like Python and a strong foundation in algebra, statistics, and calculus. Since I do not have this background I would prefer to learn more about the general concept of a neural network and less of the mathematical and programming aspects of them. Overall, it was an interesting and informative book, but I personally found it too advanced to be a beginner’s guide.

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Make Your Own Neural Network: An In-depth Visual Introduction For Beginners, by Michael Taylor PDF

Make Your Own Neural Network: An In-depth Visual Introduction For Beginners, by Michael Taylor PDF

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Download PDF GPU Computing Gems Emerald Edition (Applications of GPU Computing Series)

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Review

Praise for GPU Computing Gems: Emerald Edition: "GPU computing is becoming an outstanding field in high performance computing. Due to its easiness, the CUDA approach enables programmers to take advantage of GPU-acceleration very quickly… My research in complex science as well as applications in high frequency trading benefited significantly from GPU computing." --Dr. Tobias Preis, ETH Zurich, Switzerland "This book is an important reference for everyone working on GPU/CUDA, and contains definitive work in a selection of fields. The patterns of CUDA parallelization it describes can often be adapted to applications in other fields." --Dr. Ming Ouyang, Assistant Professor – Director Visualization and Intensive Graphics Lab, University of Louisville "Diving into the world of GPU computing has never been more important these days. GPU Computing Gems: Emerald Edition takes you through the looking glass into this fascinating world." --Martin Eisemann, Computer Graphics Lab, TU Braunschweig "…an outstanding collection of vignettes of how to program GPUs for a breathtaking range of applications." --Dr. Amitabh Varshney, Director, Institute for Advanced Computer Studies, University of Maryland "The book features a useful index that might help readers mine the gems in search of a solution to a specific algorithmic problem. The index is accompanied by online resources containing source code samples―and further information―for some of the chapters. A second volume with another 30 chapters of GPGPU application reports, somewhat more focused on generic algorithms and programming techniques, is currently in the pipeline and scheduled to appear as the "Jade Edition" sometime this month." --Computing in Science and Engineering "The book is an excellent selection of important papers describing various applications of GPUs. As such, I believe it would be a valuable addition to the bookshelf of any researcher in modeling and simulation…This is not a substitute for a more detailed text on massively parallel programming...Instead, it is a nice practical addition to that text." --Computing Reviews, August 2012 "...the perfect companion to Programming Massively Parallel Processors by Hwu & Kirk." -Nicolas Pinto, Research Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010

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From the Back Cover

Practical techniques straight from the leading minds in general purpose GPU research. Graphics Processing Units (GPUs) are designed to be parallel – having hundreds of cores versus traditional CPUs. Increasingly, you can leverage GPU power for any computationally-intense operation – not just for graphics. If you're facing the challenge of programming systems to effectively use these massively parallel processors to achieve efficiency and performance goals, GPU Computing Gems provides a wealth of tested, proven GPU techniques. Learn from the leading researchers in concurrent programming, who have gathered their insights and experience in one volume under the guidance of NVIDIA and GPU expert Wen-mei Hwu. Features Covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and more Many examples utilize NVIDIA Compute Unified Device Architecture (CUDA), the most widely-adopted GPU programming tool Offers insights and ideas as well as practical "hands-on" skills you can immediately put to use About the Editor-in-Chief Wen-Mei Hwu Co-author of Programming Massively Parallel Processors and Jerry Sanders III-Advanced Micro Devices Endowed Chair in Electrical and Computer Engineering in the Coordinated Science Laboratory of the University of Illinois at Urbana-Champaign

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Product details

Series: Applications of GPU Computing Series

Hardcover: 886 pages

Publisher: Morgan Kaufmann; 1 edition (February 7, 2011)

Language: English

ISBN-10: 0123849888

ISBN-13: 978-0123849885

Product Dimensions:

7.8 x 1.2 x 9.8 inches

Shipping Weight: 3.1 pounds (View shipping rates and policies)

Average Customer Review:

3.2 out of 5 stars

15 customer reviews

Amazon Best Sellers Rank:

#933,803 in Books (See Top 100 in Books)

Interesting topics are covered, but the variety and coverage are over reaching. This is more like a book full of articles than a useful reference. Also, I believe that you can now get all of the books on the Nvidia website for free.

Bought on the author's recommendation. It appears to contain little that I currently have a use for, though. Many items I don't have a use for, though.

I have to agree with H. Nguyen. This book is a missed opportunity. GPGPU computing is new for programmers and barely even known by scientists. The entries in this book don't really show sophisticated GPGPU philosophy or idioms. You won't read this and have "aha" moments. It would be nice if the text focused on advanced uses of segmented scan (the central trick in GPGPU computing) for load balancing and allocation, and helped the reader develop a toolbox for writing their own kernels. What's really needed is a GPU replacement for basic computer science texts like Sedgewick et. al. Just learning how to add up numbers, write a sort, write a sparse matrix code, etc, near peak efficiency of the device, is a great learning experience, because you learn to think with cooperative thread array logic rather than imperative logic. Until you master that, it's not possible to write efficient GPU code. I give the contributors credit for the articles, but I think the editorship made a mistake by not giving the book a clearer and more narrow focus. Hopefully there will soon be a book that tackles ten can't-live-without algorithms and covers them in very fine detail, addressing all performance aspects of the code and showing how coupled it is to device architecture.On the other hand I'm giving the book a second star because it does let the reader know there are others using GPGPU to solve science problems, and the topics are pretty interesting, even if the implementations are not in the GPU idiom.The best references are still the technical docs from NVIDIA and ATI (you should read both vendor's docs even if you only deal with CUDA, as extra perspective helps), the CUDA technical forum, and the handful of research papers written by good GPGPU coders (many who work at NV now).

This was the rumored successor to the GPU Gems series. I don't know if that's true. I hope not.I spend a lot of time tinkering with the GPU and sometimes I even end up using the results of these tinkerings in my day to day. The GPU Gems series have been a wonderful series that helped me understand what was possible on the GPU and where graphics is advancing. This book doesn't do any of that.I'm not really sure what the intent of this book was. There's a lot of technical information in this book, but not much of it is useful. There's a lot of complex math that I think is relevant to a small portion of people who use the GPU for GPGPU stuff. The "graphics" examples in this book are pretty a bit over the top with very little useful information. The Random Walks bit is semi interesting - but it goes into too many tangents that aren't at all relevant to why you would want to use the GPU to calculate random walks.If you do games or anything real time - this probably isn't the book for you. I think the GPU Gems and GPU Pro is where it's at.

I use GPU computing in my own research and so was eager to get my hands on this book. The authors' introduction states that they observed that while GPUs are now used in extremely diverse circumstances, many fundamental operations easily cross disciplines. Their goal therefore is to help disseminate knowledge from one area of science to others who can learn from what has already been done. This is an admirable goal with uncertain execution in this book. The text consists of 50 chapters, each chapter written by experts in their field. I can testify to the top quality of the experts contributing here from my own field of medical imaging. The chapters are well written and their variety do give a good understanding of the breathe of applications in which GPUs are finding themselves. Unfortunately, I did not learn anything new or useful that I could apply. If you are using GPUs in your field, you probably know more than this book presents. If you don't know anything about GPUs, then this book is not a good introduction. The book's audience is unclear. If you are looking for details for graphics applications this is not your book as this focuses on scientific application. I agree with several of my colleagues when they say this book should have been a GPU programming cookbook with code examples for fundamental and common operations.

I have a long programming background with everything from micro-controller programming to SAAS. I was interested in this book as way to get an introduction to programming techniques and what problem domains GPU-based computing was helpful in.The book was not what I expected it to be. It is a large collection of graduate level papers on problems that GPU-based computing has been employed to help solve. The information about what is GPU-based programming and what approaches / techniques are used to do GPU-based computing you will have to figure out yourself from trying to understanding the underlying principles that are rarely directly explained. The book does give you a very wide range of what problems and problem domains are good to approach from a GPU-based approach.If you want to find out about parallel algorithm development/design, then this book is not a good resource.Each chapter has:1) Introduction, problem statement and context2) Core Methods3) Algorthims, implementations and evaluations4) Final evaluation5) Future directionThe chapter contents sound good but the level of detail in each part is generally not good for initial learning of concepts. Rather, if you have some background in the problem domain or GPU-based computing, then the information would be rather helpful to you.I am going to keep this book on my reference bookcase as I think it provides some useful starting points for research as well as examples of where GPU-based computing works well.

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Product details

Paperback: 112 pages

Publisher: Independently published (February 2, 2019)

Language: English

ISBN-10: 1795423323

ISBN-13: 978-1795423328

Product Dimensions:

8.5 x 0.3 x 11 inches

Shipping Weight: 12.5 ounces (View shipping rates and policies)

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About the Author

Pamela Hunt, BS, MSN, RN, is vice president of patient services/chief nursing executive at The Indiana Heart Hospital in Indianapolis. She has lectured nationally and internationally on the challenges of nursing leadership. Her articles regarding healthcare leadership have been published in nationally recognized journals, including Nursing Management, Dimensions of Critical Care Nursing, OR Nursing, and AORN Journal. She authored the learning module entitled 'The Nurse Manager and Business Plan Development with Cost-Benefit Analysis' for Sigma Theta Tau. Deborah Laughon, RN, BSN, MS, DBA, CCRN, is the ANCC Magnet Recognition Progam® manager at Florida Hospital. She has a unique background combining cardiac, acute, and emergency care clinical expertise with broad analytical and financial management in a large community institution. She coordinates publications and research to support evidence-based practice and extends the science of nursing to achieve ANCC Magnet Recognition Program® designation. Previously, Laughon served as director of emergency and cardiac service, patient flow, systems improvement manager, and clinical manager for the coronary care unit, cardiac interventional unit, adult admissions unit, catheter lab recovery unit, and clinical dietitians. Laughon also serves as faculty for the University of Phoenix, Central Florida and College of Natural Science and Nursing. She has been published in national healthcare and nursing journals and has presented to healthcare organizations nationwide and for national professional associations.

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Product details

Perfect Paperback: 150 pages

Publisher: HCPro Inc.; 1 edition (September 22, 2011)

Language: English

ISBN-10: 1601468695

ISBN-13: 978-1601468697

Product Dimensions:

8.5 x 0.8 x 11.2 inches

Shipping Weight: 14.4 ounces (View shipping rates and policies)

Average Customer Review:

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Amazon Best Sellers Rank:

#1,233,674 in Books (See Top 100 in Books)

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