LEADER 03045nam 22005415 450 001 9910483468203321 005 20251202165401.0 010 $a9781484261682 010 $a1484261682 024 7 $a10.1007/978-1-4842-6168-2 035 $a(CKB)5460000000008571 035 $a(DE-He213)978-1-4842-6168-2 035 $a(MiAaPQ)EBC6450882 035 $a(CaSebORM)9781484261682 035 $a(PPN)253256461 035 $a(Perlego)4513700 035 $a(EXLCZ)995460000000008571 100 $a20210104d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConvolutional Neural Networks with Swift for Tensorflow $eImage Recognition and Dataset Categorization /$fby Brett Koonce 205 $a1st ed. 2021. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2021. 215 $a1 online resource (XXI, 245 p. 1 illus.) 311 08$a9781484261675 311 08$a1484261674 327 $aChapter 1: MNIST: 1D Neural Network -- Chapter 2: MNIST: 2D Neural Network -- Chapter 3: CIFAR: 2D Nueral Network with Blocks -- Chapter 4: VGG Network -- Chapter 5: Resnet 34 -- Chapter 6: Resnet 50 -- Chapter 7: SqueezeNet -- Chapter 8: MobileNrt v1 -- Chapter 9: MobileNet v2 -- Chapter 10: Evolutionary Strategies -- Chapter 11: MobileNet v3 -- Chapter 12: Bag of Tricks -- Chapter 13: MNIST Revisited -- Chapter 14: You are Here. 330 $aDive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning. This book uses convolutional neural networks to do image recognition all in the familiar and easy to work with Swift language. It begins with a basic machine learning overview and then ramps up to neural networks and convolutions and how they work. Using Swift and Tensorflow, you'll perform data augmentation, build and train large networks, and build networks for mobile devices. You?ll also cover cloud training and the network you build can categorize greyscale data, such as mnist, to large scale modern approaches that can categorize large datasets, such as imagenet. Convolutional Neural Networks with Swift for Tensorflow uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field. You will: Categorize and augment datasets Build and train large networks, including via cloud solutions Deploy complex systems to mobile devices. 606 $aApple computer 606 $aMachine learning 606 $aApple and iOS 606 $aMachine Learning 615 0$aApple computer. 615 0$aMachine learning. 615 14$aApple and iOS. 615 24$aMachine Learning. 676 $a006.32 700 $aKoonce$b Brett$01229657 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483468203321 996 $aConvolutional neural networks with Swift for Tensorflow$92854307 997 $aUNINA