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Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks, by Timothy Masters
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News flash... If anyone would prefer reading these books in Korean, Volume 1 is now available from a South Korean publisher, with Volumes 2 and 3 available soon: http://www.acornpub.co.kr/book/dbn-cuda-vol1 Deep belief nets are one of the most exciting recent developments in artificial intelligence. The structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ‘thought process’ that is capable of learning abstract concepts built from simpler primitives. A typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. This book presents the essential building blocks of the most common forms of deep belief nets. At each step the text provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the DEEP program which implements these algorithms, are available for free download from the author’s website. NOTE... The source code available for free download includes all of the code listed in the book, along with some libraries of related routines. Complete code for the DEEP program is not included; this code is enormous, as it includes many Windows-only interface routines, screen display code, and so forth. Users who wish to write their own DBN programs are responsible for implementing their own hardware/OS interface, while using my supplied code for the mathematical calculations.
- Sales Rank: #207684 in Books
- Published on: 2015-02-11
- Original language: English
- Number of items: 1
- Dimensions: 9.69" h x .55" w x 7.44" l, .97 pounds
- Binding: Paperback
- 244 pages
About the Author
Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored five books on practical applications of predictive modeling: Practical Neural Network Recipes in C++ (Academic Press, 1993) Signal and Image Processing with Neural Networks (Wiley, 1994) Advanced Algorithms for Neural Networks (Wiley, 1995) Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995) Assessing and Improving Prediction and Classification (CreateSpace, 2013)
Most helpful customer reviews
12 of 13 people found the following review helpful.
Poor Self-Publishing Effort -- Many Better Resources Available
By Abel Brown
I really wanted to like these books but the quality is just too low. TM thinks that just b/c some C++ code is included that the writing doesn't matter. The explanations of core concepts are terrible. There are many typos and confusing sentences and even whole paragraphs that just don't make sense. Much of the content for the other two books just copies content word for word from each other. There is very little in the other two volumes. Literally, whole sections are copied and pasted into Vol 2 and Vol 3. There a very few diagrams of anything and the diagrams and graphs that are included are of such low quality as to not be useful. I really want to support self publishing but these books are basically C++ code documentation. With all the DNN frameworks available such as Caffe, Torch, Theano, TensorFlow, CNTK there really isn't much point in studying this guys C++ code. Not to mention there is cuDNN with many of these core operations implemented.
There are many good resources on the internet that are of much higher quality. Checkout Michael Nielsen's free on-line book, also deep learning dot net has many good resources. Additionally NVIDIA offers self-study course for deep learning (just google) and also their deep learning institute (again just google).
17 of 25 people found the following review helpful.
Deep Belief (Learning) Networks Are An Important Advance in Machine Learning
By Hood River Trading
First, I must disclose that I have known Dr. Masters for 20 years and have collaborated with him on various projects including a book we co-authored.(Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments). In addition he was a crucial adviser on my book Evidence Based Technical Analysis. He is also a friend.
With that said, Dr. Masters is a person of integrity, humility, intellectual honesty and competence. When he became interested in Deep Belief Networks, also known as Deep Learning Nets, I took that as a signal that this was a truly important development in the field of machine learning and I’d better get my admittedly slow human intellect exposed to DBNs.
Amazon allows you to look inside the book so I won't reiterate the table of contents or outline what the book contains. Interested readers can do that themselves.
The key point for interested readers is this: deep belief networks represent an important advance in machine learning due to their ability to autonomously synthesize features. Feature engineering, the creating of candidate variables from raw data, is the key bottleneck in the application of machine learning to any field. If feature engineering is done well, even a relatively weak model, such a multiple linear regression can produce a useful predictive model. If done poorly, even the most powerful machine learning methods will fail. Thus feature engineering is the "without-which-not" of success. Of particular importance is that the feature engineering conducted by a DBN is performed in an unsupervised fashion ( no reference to the target variable). Thus if it takes a DBN numerous layers to self-organize the critical problem features, there is no risk of over-fitting was would occur if the target variable were to be considered during this phase of model training. The target variable is only considered by a DBN after feature engineering is complete. Of course this threatens the key role currently played by "domain experts" upon whom the feature engineering task currently falls. These people may find themselves on the same unemployment line as truck-drivers put out of work by self-driving 18 wheelers, and tax advisors whose job has been usurped by IBM’s Watson computer.
What this book does is bring you up to speed in this very exciting area of machine learning. Background material is written in an intuitive fashion that allows the non-expert reader to grasp the big ideas. But then there is in depth discussion of theory, application and actual code for the expert.
David R. Aronson
0 of 0 people found the following review helpful.
Absolutely Amazing.
By User
This book is absolutely amazing. There are people that use prebuilt machine learning libraries and then there are those that actually make those libraries. As a result from studying this book my machine learning models have not only dramatically improved in accuracy but they have dramatically improved in execution speed as well. This is an absolute must have for any machine learning / artificial intelligence practitioner.
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