Learning Deep Learning

<p><strong>NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results</strong> <p/></p> 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. <br>-- <em>From the foreword by Dr. Anima Anandkumar Bren Professor Caltech and Director of ML Research NVIDIA</em> <p/>Ekman 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. <br>-- <em>From the foreword by Dr. Craig Clawson Director NVIDIA Deep Learning Institute</em> <p><br>Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. <em><strong>Learning Deep Learning</strong></em> 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. <p/>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 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. <p/>Throughout 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.</p> <ul> <li>Explore and master core concepts: perceptrons gradient-based learning sigmoid neurons and back propagation</li> <li>See how DL frameworks make it easier to develop more complicated and useful neural networks</li> <li>Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis</li> <li>Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences</li> <li>Master NLP with sequence-to-sequence networks and the Transformer architecture</li> <li>Build applications for natural language translation and image captioning</li> </ul> <p><em>NVIDIA'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.</em> <p/><strong><em>Register your book for convenient access to downloads updates and/or corrections as they become available. See inside book for details.</em></strong></p>