u003cpu003eNVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results u003cpu003eu003c/pu003e To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals. u003cbru003e-- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA u003cpu003eEkman uses a learning technique that in our experience has proven pivotal to success--asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us. u003cbru003e-- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute u003cpu003eu003cbru003eDeep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. u003cpu003eAfter 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, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. u003cpu003eThroughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning.u003c/pu003e u003culu003e u003cliu003eExplore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagationu003c/liu003e u003cliu003eSee how DL frameworks make it easier to develop more complicated and useful neural networksu003c/liu003e u003cliu003eDiscover how convolutional neural networks (CNNs) revolutionize image classification and analysisu003c/liu003e u003cliu003eApply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequencesu003c/liu003e u003cliu003eMaster NLP with sequence-to-sequence networks and the Transformer architectureu003c/liu003e u003cliu003eBuild applications for natural language translation and image captioningu003c/liu003e u003c/ulu003e u003cpu003eNVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. u003cpu003eRegister your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.u003c/pu003e
