What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB.
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ISBN-13: 9783030853648, 978-3030853648. Deep Learning, AI and robotics represent key ingredients for the 4th Industrial Revolution. Format: Paperback.
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ISBN-13: 9789811563232, 978-9811563232. This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics.
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From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy.
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Title:-Machine Learning Mathematics: Study Deep Learning Through Data Science. How to Build Artificial Intelligence Through Concepts of Statistics, Algorithms, Analysis and Data Mining Author:-Samuel Hack Format:-Paperback ISBN13:-9781699742150 Language:-English Publisher:-Independently Published Publication Date:-2019 Pages:-108 Pages Category:-Mathematics Textbook Condition:-Brand New
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About the Author Jeff Heaton, PhD, is a computer scientist that specializes in data science and artificial intelligence. Specializing in Python, R, Java and C#, he is an open source contributor and author of more than ten books. His areas of expertise include predictive modeling, data mining, big data, business intelligence, and artificial intelligence. Jeff holds a Master's Degree in Information Management from Washington University and a PhD in computer science from Nova Southeastern University in computer science. He is the lead developer for the Encog Machine Learning Framework open source project, a senior member of IEEE, and a fellow of the Life Management Institute (FLMI).
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ISBN-13: 9781484253489, 978-1484253489. Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects.
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Introduction. The Instruction Paradigm. The Deep Learning Process and the Construction of Knowledge. Goals and Performance. Collaborative Learning. Academic Reading and Deep Learning. Deep Writing. Inclusive Deep Learning Environments. Time and Deep Learning: Intensive Teaching. Internationalization and Deep Learning. Evaluating to Learn. Student Evaluation of Teaching: Evaluating Deep Learning. The Learning Paradigm. Final Summary.
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