Deep Belief Nets in C++ and Cuda C: Volume 3: Convolutional Nets - Paperback

by Timothy Masters (Author)

Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how 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. These models are especially useful for image processing applications.

• Discover convolutional nets and how to use them
  
• Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs
  
• Master the various programming algorithms required
  
• Carry out multi-threaded gradient computations and memory allocations for this threading
  
• Work with CUDA code implementations of all core computations, including layer activations and gradient calculations
  
• Make use of the CONVNET program and manual to explore convolutional nets and case studies
  

Back Jacket

Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how 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. These models are especially useful for image processing applications.
At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents 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 executable CONVNET program which implements these algorithms, are available for free download.

• Discover convolutional nets and how to use them
  
• Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs
  
• Master the various programming algorithms required
  
• Carry out multi-threaded gradient computations and memory allocations for this threading
  
• Work with CUDA code implementations of all core computations, including layer activations and gradient calculations
  
• Make use of the CONVNET program and manual to explore convolutional nets and case studies
  

Author Biography

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); Data Mining Algorithms in C++ (Apress, 2018); Assessing and Improving Prediction and Classification (Apress, 2018); Deep Belief Nets in C++ and CUDA C: Volume 1 (Apress, 2018); and Deep Belief Nets in C++ and CUDA C: Volume 2 (Apress, 2018).