Deep Learning Scratch by Seth Weidman (36 results)

Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

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Paperback or Softback. Condition: New. Deep Learning from Scratch: Building with Python from First Principles. Book.

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Condition: As New. Unread book in perfect condition.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: New. 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.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.This book provides:Extremely clear and thorough mental models-accompanied by working code examples and mathematical explanations-for understanding neural networksMethods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented frameworkWorking implementations and clear-cut explanations of convolutional and recurrent neural networksImplementation of these neural network concepts using the popular PyTorch framework.

Condition: New.

Paperback. Condition: new. Paperback. 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. Youll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. Youll 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, youll be set up for success on all future deep learning projects.This book provides:Extremely clear and thorough mental modelsaccompanied by working code examples and mathematical explanationsfor understanding neural networksMethods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented frameworkWorking implementations and clear-cut explanations of convolutional and recurrent neural networksImplementation of these neural network concepts using the popular PyTorch frameworkAbout the AuthorSeth is a data scientist who lives in San Francisco. He has been obsessed with understanding Deep Learning ever since he began learning about it in late 2016 and has been writing and speaking about it whenever he can ever since. Professionally, he has applied a variety of machine learning models in industry, taught data science to individuals and companies, and works on modeling and Python projects on the side. Full time, he teaches data science to companies via the Corporate Training team at Metis. He strives to find the simplicity on the other side of complexity. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: new.

Condition: New.

Paperback. Condition: New. 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.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.This book provides:Extremely clear and thorough mental models-accompanied by working code examples and mathematical explanations-for understanding neural networksMethods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented frameworkWorking implementations and clear-cut explanations of convolutional and recurrent neural networksImplementation of these neural network concepts using the popular PyTorch framework.

Condition: New. In.

Paperback. Condition: New.

Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days. 526.

Condition: As New. Unread book in perfect condition.

Condition: New. 2019. Paperback. . . . . .

Condition: New. 2019. Paperback. . . . . . Books ship from the US and Ireland.

Condition: New. pp. 252.

Condition: New. pp. 252.

Condition: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Condition: NEW.

Paperback. Condition: Brand New. 235 pages. 9.00x7.00x0.50 inches. In Stock.

Paperback. Condition: New. 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.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.This book provides:Extremely clear and thorough mental models-accompanied by working code examples and mathematical explanations-for understanding neural networksMethods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented frameworkWorking implementations and clear-cut explanations of convolutional and recurrent neural networksImplementation of these neural network concepts using the popular PyTorch framework.

Paperback. Condition: new. Paperback. 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. Youll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. Youll 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, youll be set up for success on all future deep learning projects.This book provides:Extremely clear and thorough mental modelsaccompanied by working code examples and mathematical explanationsfor understanding neural networksMethods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented frameworkWorking implementations and clear-cut explanations of convolutional and recurrent neural networksImplementation of these neural network concepts using the popular PyTorch frameworkAbout the AuthorSeth is a data scientist who lives in San Francisco. He has been obsessed with understanding Deep Learning ever since he began learning about it in late 2016 and has been writing and speaking about it whenever he can ever since. Professionally, he has applied a variety of machine learning models in industry, taught data science to individuals and companies, and works on modeling and Python projects on the side. Full time, he teaches data science to companies via the Corporate Training team at Metis. He strives to find the simplicity on the other side of complexity. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condition: New. 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 mach.