Manning, 2018. — 325 p. Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind...
Manning Publications, 2018. — 392 p. — ISBN13: 978-1617295546. Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Machine learning has made remarkable progress in recent years. Deep-learning systems now...
LazyProgrammer, 2016. — 59 p. This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural...
Amazon Digital Services LLC, 2018. — 98 p. — ASIN: B07K2Q6DXH. Deep learning is a process that widens the range of most artificial intelligence problems like speech recognition, image classification, question answering, optical character recognition, and transforming text to speech. It is true that deep learning is a complex subject to learn and understand, but it is not...
LazyProgrammer, 2016. — 71 p. Deep learning is making waves. At the time of this writing (March 2016), Google’s AlghaGo program just beat 9-dan professional Go player Lee Sedol at the game of Go, a Chinese board game. Experts in the field of Artificial Intelligence thought we were 10 years away from achieving a victory against a top professional Go player, but progress seems to...
Packt Publishing, 2018. — 420 p. — ISBN: 978-1-78588-036-0. Get to grips with the essentials of deep learning by leveraging the power of Python Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training...
Applied Data Science Partners Ltd, 2019. — 308 p. — ISBN: 978-1-492-04194-8. Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive...
Springer Nature Singapore Pte Ltd., 2019. — 237 p. — ISBN: 978-981-10-5152-4 (eBook). This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and...
Packt Publishing, 2018. — 496 p. — ISBN: 978-1-78899-745-4. Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open-source APIs Build and deploy powerful neural network models using the latest Java deep learning libraries Java is one of the most widely used programming languages. With the rise of deep learning, it has become...
The MIT Press, 2019. — 196 p. — (MIT Press Essential Knowledge series). — ISBN: 978-0262537551. An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones,...
Apress, 2017. — 169 p. — ISBN-13 (electronic): 978-1-4842-2766-4. Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of...
LazyProgrammer, 2016. — 46 p. When we talk about modern deep learning, we are often not talking about vanilla neural networks - but newer developments, like using Autoencoders and Restricted Boltzmann Machines to do unsupervised pre-training. Deep neural networks suffer from the vanishing gradient problem, and for many years researchers couldn’t get around it - that is, until...
Springer Nature, 2019. — 452 p. — ISBN: 978-3-030-13969-8 (eBook). This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It...
Apress, 2018. — 200 p. — ISBN-13 (electronic): 978-1-4842-3591-1. Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive...
Apress, 2018. — 262 p. — ISBN-13 (electronic): 978-1-4842-3646-8. Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You’ll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for...
Apress, 2018. — 165 p. — ISBN-13 (electronic): 978-1-4842-3721-2. Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ‘thought process’ that is capable of learning abstract...
Apress, 2018. — 425 p. — ISBN13: 978-1-4842-3790-8. Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You'll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and...
John Wiley & Sons, Inc, 2019. — 442 p. — ISBN: 978-1-119-54304-6. Take a deep dive into deep learning. Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic — and all of the underlying technologies associated with...
Amazon Digital Services LLC, 2017. — 75 p. — ISBN: 1981614060. Neural Networks and Deep Learning: Deep Learning explained to your granny – A visual introduction for beginners who want to make their own Deep Learning Neural Network (Machine Learning) Ready to crank up a neural network to get your self-driving car pick up the kids from school? Want to add 'Deep Learning’ to your...
O’Reilly, 2019. — 253 p. — ISBN13: 978-1-492-03983-9 Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields....
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