LEADER 03773nam 2200421 450 001 9910826422503321 005 20200302094444.0 010 $a1-78995-271-9 035 $a(CKB)4100000010011028 035 $a(MiAaPQ)EBC6005548 035 $a(CaSebORM)9781789956177 035 $a(PPN)242831710 035 $a(EXLCZ)994100000010011028 100 $a20200302d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced deep learning with Python $edesign and implement advanced next-generation AI solutions using TensorFlow and Pytorch /$fIvan Vasilev 205 $a1st edition 210 1$aBirmingham, England ;$aMumbai :$cPackt,$d[2019] 210 4$dİ2019 215 $a1 online resource (456 pages) 311 $a1-78995-617-X 320 $aIncludes bibliographical references. 330 $aGain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you'll need to understand everything from how neural networks work to training CNN models. In this book, you'll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You'll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you'll focus on variational autoencoders and GANs. You'll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You'll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you'll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you'll understand how to apply deep learning to autonomous vehicles. By the end of this book, you'll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to furthe... 606 $aPython (Computer program language) 615 0$aPython (Computer program language) 676 $a005.133 700 $aVasilev$b Ivan$01691144 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826422503321 996 $aAdvanced deep learning with Python$94067331 997 $aUNINA