LEADER 03624nam 2200469 450 001 9910523769603321 005 20220819190637.0 010 $a1-4842-7413-X 024 7 $a10.1007/978-1-4842-7413-2 035 $a(MiAaPQ)EBC6810886 035 $a(Au-PeEL)EBL6810886 035 $a(CKB)19919639500041 035 $a(OCoLC)1287136805 035 $a(OCoLC)1286665297 035 $a(OCoLC-P)1286665297 035 $a(CaSebORM)9781484274132 035 $a(EXLCZ)9919919639500041 100 $a20220819d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModern deep learning design and application development $eversatile tools to solve deep learning problems /$fAndre Ye 210 1$aNew York, New York :$cApress,$d[2022] 210 4$dİ2022 215 $a1 online resource (463 pages) 300 $aIncludes index. 311 08$aPrint version: Ye, Andre Modern Deep Learning Design and Application Development Berkeley, CA : Apress L. P.,c2021 9781484274125 327 $aChapter 1: A Deep Dive Into Keras -- Chapter 2: Pre-training Strategies and Transfer Learning -- Chapter 3: The Versatility of Autoencoders -- Chapter 4: Model Compression for Practical Deployment -- Chapter 5: Automating Model Design with Meta-Optimization -- Chapter 6:Successful Neural Network Architecture Design -- Chapter 7:Reframing Difficult Deep Learning Problems. 330 $aLearn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. Youll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, youll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. Youll learn not only to understand and apply methods successfully but to think critically about it. Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to todays difficult problems. You will: Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches. 606 $aDeep learning (Machine learning) 615 0$aDeep learning (Machine learning) 676 $a006.31 700 $aYe$b Andre$01077588 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910523769603321 996 $aModern Deep Learning Design and Application Development$92588965 997 $aUNINA