Deep Learning from Scratch - by Seth Weidman (Paperback)
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<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You'll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.</p><p>Author Seth Weidman shows you how neural networks work using a first principles approach. You'll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you'll be set up for success on all future deep learning projects.</p><p>This book provides: </p><ul><li>Extremely clear and thorough mental models--accompanied by working code examples and mathematical explanations--for understanding neural networks</li><li>Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework</li><li>Working implementations and clear-cut explanations of convolutional and recurrent neural networks</li><li>Implementation of these neural network concepts using the popular PyTorch framework</li></ul><p/><br></br><p><b> About the Author </b></p></br></br><p>Seth Weidman is a data scientist who has applied and taught machine learning concepts for several years. He started out as the first data scientist at Trunk Club, where he built lead scoring models and recommender systems, and currently works at Facebook, where he builds machine learning models for their infrastructure team. In between he taught data science and machine learning for the bootcamps and on the corporate training team at Metis. He is passionate about explaining complex concepts simply, striving to find the simplicity on the other side of complexity.</p>
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