Saitoh Koki (22 results)
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.Lucky's Textbooks
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- Softcover
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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Paperback or Softback. Condition: New. Deep Learning from the Basics: Python and Deep Learning: Theory and Implementation. Book.
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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Paperback. Condition: New. Discover ways to implement various deep learning algorithms by leveraging Python and other technologiesKey FeaturesLearn deep learning models through several activitiesBegin with simple machine learning problems, and finish by building a complex system of your ownTeach your machines to see by mastering… the technologies required for image recognitionBook DescriptionDeep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation-an efficient way to calculate the gradients of weight parameters-and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning.What you will learnUse Python with minimum external sources to implement deep learning programsStudy the various deep learning and neural network theoriesLearn how to determine learning coefficients and the initial values of weightsImplement trends such as Batch Normalization, Dropout, and AdamExplore applications like automatic driving, image generation, and reinforcement learningWho this book is forDeep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.
- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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£ 45.90
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Paperback. Condition: New. Discover ways to implement various deep learning algorithms by leveraging Python and other technologiesKey FeaturesLearn deep learning models through several activitiesBegin with simple machine learning problems, and finish by building a complex system of your ownTeach your machines to see by mastering… the technologies required for image recognitionBook DescriptionDeep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation-an efficient way to calculate the gradients of weight parameters-and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning.What you will learnUse Python with minimum external sources to implement deep learning programsStudy the various deep learning and neural network theoriesLearn how to determine learning coefficients and the initial values of weightsImplement trends such as Batch Normalization, Dropout, and AdamExplore applications like automatic driving, image generation, and reinforcement learningWho this book is forDeep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condition: New. pp. 316.
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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£ 35.44
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Condition: New. In.
- Softcover
Seller: Chiron Media, Wallingford, , United KingdomChiron Media
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£ 32.12
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Paperback. Condition: New.
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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£ 38.03
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Condition: As New. Unread book in perfect condition.
- Softcover
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
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£ 40.10
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Paperback. Condition: New. Discover ways to implement various deep learning algorithms by leveraging Python and other technologiesKey FeaturesLearn deep learning models through several activitiesBegin with simple machine learning problems, and finish by building a complex system of your ownTeach your machines to see by mastering… the technologies required for image recognitionBook DescriptionDeep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation-an efficient way to calculate the gradients of weight parameters-and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning.What you will learnUse Python with minimum external sources to implement deep learning programsStudy the various deep learning and neural network theoriesLearn how to determine learning coefficients and the initial values of weightsImplement trends such as Batch Normalization, Dropout, and AdamExplore applications like automatic driving, image generation, and reinforcement learningWho this book is forDeep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.
- Softcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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£ 40.94
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Paperback. Condition: New. Discover ways to implement various deep learning algorithms by leveraging Python and other technologiesKey FeaturesLearn deep learning models through several activitiesBegin with simple machine learning problems, and finish by building a complex system of your ownTeach your machines to see by mastering… the technologies required for image recognitionBook DescriptionDeep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation-an efficient way to calculate the gradients of weight parameters-and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning.What you will learnUse Python with minimum external sources to implement deep learning programsStudy the various deep learning and neural network theoriesLearn how to determine learning coefficients and the initial values of weightsImplement trends such as Batch Normalization, Dropout, and AdamExplore applications like automatic driving, image generation, and reinforcement learningWho this book is forDeep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.
- Softcover
- Print on Demand
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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£ 40.08
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Softcover
- Print on Demand
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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£ 35.71
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PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, , United KingdomMajestic Books
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£ 41.79
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Condition: New. Print on Demand pp. 316.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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£ 43.25
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Condition: New. PRINT ON DEMAND pp. 316.
- Softcover
- Print on Demand
Seller: THE SAINT BOOKSTORE, Southport, , United KingdomTHE SAINT BOOKSTORE
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£ 40.13
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Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
- Softcover
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Seller: moluna, Greven, , Germanymoluna
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£ 44.32
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Deep Learning from the Basics provides a step-by-step guide to these complex algorithms, from the ground up. From the basics of machine learning to multilayer neural networks, this book presents a thorough overview of… these technologies enabling you to impl.
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- Print on Demand
Seller: preigu, Osnabrück, Germanypreigu
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£ 46.03
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Taschenbuch. Condition: Neu. Deep Learning from the Basics | Python and Deep Learning: Theory and Implementation | Koki Saitoh | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Packt Publishing | EAN 9781800206137 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de |… Anbieter: preigu Print on Demand.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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£ 53.98
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us.Deep Learnin…g from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation-an efficient way to calculate the gradients of weight parameters-and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning.
