Magnus Ekman

Broschiertes Buch

Learning Deep Learning

Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow

Beschreibung

NVIDIA's Full-Color Guide to Deep Learning: All StudentsNeed to Get Started and Get Results Learning Deep Learning is a complete guide to DL.Illuminating both the core concepts and the hands-on programming techniquesneeded to succeed, this book suits seasoned developers, data scientists,analysts, but also those with no prior machine learning or statisticsexperience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers,Magnus Ekman shows how to use them to build advanced architectures, includingthe Transformer. He describes how these concepts are used to build modernnetworks for computer vision and natural language processing (NLP), includingMask R-CNN, GPT, and BERT. And he explains how a natural language translatorand a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples usingTensorFlow with Keras. Corresponding PyTorch examples are provided online, andthe book thereby covers the two dominating Python libraries for DL used inindustry and academia. He concludes with an introduction to neural architecturesearch (NAS), exploring important ethical issues and providing resources forfurther learning. * Exploreand master core concepts: perceptrons, gradient-based learning, sigmoidneurons, and back propagation * See how DL frameworks make it easier to developmore complicated and useful neural networks * Discover how convolutional neuralnetworks (CNNs) revolutionize image classification and analysis * Apply recurrentneural networks (RNNs) and long short-term memory (LSTM) to text and othervariable-length sequences * Master NLP with sequence-to-sequence networks and theTransformer architecture * Build applications for natural language translation andimage captioning

Autor

Magnus Ekman, Ph.D., is a director of architecture at NVIDIA Corporation. His doctorate is in computer engineering, and he is the inventor of multiple patents. He was first exposed to artificial neural networks in the late nineties in his native country, Sweden. After some dabbling in evolutionary computation, he ended up focusing on computer architecture and relocated to Silicon Valley, where he lives with his wife Jennifer, children Sebastian and Sofia, and dog Babette. He previously worked with processor design and R&D at Sun Microsystems and Samsung Research America, and has been involved in starting two companies, one of which (Skout) was later acquired by The Meet Group, Inc. In his current role at NVIDIA, he leads an engineering team working on CPU performance and power efficiency for system on chips targeting the autonomous vehicle market. As the Deep Learning (DL) field exploded the past few years, fueled by NVIDIA's GPU technology and CUDA, Dr. Ekman found himself in the middle of a company expanding beyond computer graphics into becoming a deep learning (DL) powerhouse. As a part of that journey, he challenged himself to stay up-to-date with the most recent developments in the field. He considers himself to be an educator, and in the process of writing Learning Deep Learning (LDL), he partnered with the NVIDIA Deep Learning Institute (DLI), which offers hands-on training in AI, accelerated computing, and accelerated data science. He is thrilled about DLI's plans to add LDL to its existing portfolio of self-paced online courses, live instructor-led workshops, educator programs, and teaching kits.

Produktdetails

• Verlag: Pearson / Pearson Education Limited
• Seitenzahl: 752
• Erscheinungstermin: 17. August 2021
• Englisch
• Abmessung: 236mm x 200mm x 52mm
• Gewicht: 1180g
• ISBN-13: 9780137470358
• ISBN-10: 0137470355
• Artikelnr.: 61343980