💰 Playing Game with Deep Convolutional Neural Networks Trained by Supervised Learning

Matching problems between operating system Ubuntu, Windows 10 , library CUDA, cudnnlib , and machine learning framework pytorch, keras, chainer are discussed.{/INSERTKEYS}{/PARAGRAPH} Three frameworks including pytorch, keras, and chainer for machine learning on CUDA and cudnnlib will be introduced. The result of MNIST benchmark for machine learning shows that GPU of a single GeForce GTX Ti board takes only less than 48 seconds while the INTEL i7 quad-core CPU requires 15 minutes and 42 seconds. {PARAGRAPH}{INSERTKEYS}All you need to do is to install GPU-enabled software for parallel computing. Two operating system examples including Ubuntu This book shows how to install CUDA and cudnnlib in two operating systems. The GPU parallel computer is suitable for machine learning, deep neural network learning. The GPU parallel computer is based on SIMD single instruction, multiple data computing. CUDA is a parallel computing platform and application programming interface API model created by Nvidia. The power consumption of GPU is so large that we should take care of the temperature and heat from the GPU board in the single box. A minimum GPU parallel computer is composed of a CPU board and a GPU board. A CUDA core is most commonly referring to the single-precision floating point units in an SM streaming multiprocessor. A CUDA core can initiate one single precision floating point instruction per clock cycle. for image processing published in 1. For example, GeForce GTX Ti is a GPU board with CUDA cores. Software installation is another critical issue for machine learning in Python. The first GPU for neural networks was used by Kyoung-Su Oh, et al. Imagine that we are in the midst of a parallel computing era. Using the GeForce GTX Ti, the performance is roughly 20 times faster than that of an INTEL i7 quad-core CPU. We have benchmarked the MNIST hand-written digits recognition problem 60, persons: hand-written digits from 0 to 9. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit GPU for general purpose processing. Our goal is to have the faster parallel computer with lower power dissipation.