ISBN: 9798185504710 - deep learning for absolute beginners: neural networks from scratch with python and tensorflow (data science foundations series) (6 resultados)

ISBN: 
Refinar con la Búsqueda avanzada

Filtrar la búsqueda

  • Libros (6)

  • Nuevo (6)

a

Intervalo de precios personalizado (EUR)

a

  • Idioma: Inglés

    Editorial: Independently Published Jul 2026, 2026

    9798185504710

    Serie: Libro 2 de 7 - Data Science Foundations Series

    • Tapa blanda

    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 32,79

    Envío por EUR 35,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 2 disponibles

    Taschenbuch. Condición: Neu. Neuware.

  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798185504710

    Serie: Libro 2 de 7 - Data Science Foundations Series

    • Tapa blanda
    • Impresión bajo demanda

    Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 20,18

     Gastos de envío gratis 
    Se envía dentro de Estados Unidos de America

    Cantidad disponible: 1 disponible

    Paperback. Condición: new. Paperback. Build a neural network that recognizes images and understands text - starting from a single neuron. Deep learning has a reputation for being impenetrable: the domain of specialists fluent in calculus most readers don't have. Deep Learning for Absolute Beginners proves that reputation wrong. Every idea - a neuron, a layer, backpropagation, a loss function - is built up from plain language and a concrete analogy, then made real with runnable Python and TensorFlow/Keras code, before any formula appears. This isn't a theory book. It's a build-it-yourself book. What You'll Actually BuildYour first working neural network, trained and evaluated in KerasA real image classifier using Convolutional Neural Networks (CNNs)A transfer learning project using a pretrained modelSequential models - RNNs, LSTMs, and GRUs - for time-dependent dataA natural language processing pipeline, from raw text to predictionsA working introduction to Transformers and attention - the architecture behind modern AIA complete, end-to-end deep learning project, entirely your own Why This Book Is DifferentBackpropagation and gradient descent, genuinely explained - not waved at, not skipped, actually understood.An honest comparison with classical machine learning. You'll learn exactly when a neural network is the right tool - and when a random forest still wins.Regularization gets a full chapter, so your models generalize instead of memorizing.Computer vision and NLP both covered in depth - not just one, with the other as an afterthought.Worked solutions for every exercise, across all sixteen chapters.Who This Book Is For Written for readers who already have working comfort with Python, NumPy, pandas, and core machine learning vocabulary - training and test sets, overfitting, evaluation metrics (Book 1 of this series, Machine Learning for Absolute Beginners, builds exactly this foundation if you're starting fresh). If you can train and evaluate a scikit-learn model, you're ready for this book. You do not need prior exposure to neural networks, calculus beyond an intuitive sense of a derivative, or advanced linear algebra. Every mathematical idea is grounded in working code before it's ever discussed abstractly. What You'll Be Able to Do By the final chapter, you'll be able to design a neural network architecture for a given problem, train it without it overfitting, debug a model that refuses to learn, build genuine image classifiers and text models, and understand - concretely, not just by reputation - how today's AI systems actually work underneath. Deep learning is not magic, and it is not only for specialists. It's a buildable skill. This book builds it, one carefully explained layer at a time. Scroll up and start training your first neural network today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798185504710

    Serie: Libro 2 de 7 - Data Science Foundations Series

    • Tapa blanda
    • Impresión bajo demanda

    Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 20,19

     Gastos de envío gratis 
    Se envía dentro de Estados Unidos de America

    Cantidad disponible: Más de 20 disponibles

    Condición: New. Print on Demand.

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798185504710

    Serie: Libro 2 de 7 - Data Science Foundations Series

    • Tapa blanda
    • Impresión bajo demanda

    Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 22,19

     Gastos de envío gratis 
    Se envía dentro de Estados Unidos de America

    Cantidad disponible: Más de 20 disponibles

    PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798185504710

    Serie: Libro 2 de 7 - Data Science Foundations Series

    • Tapa blanda
    • Impresión bajo demanda

    Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 19,29

    Envío por EUR 4,96 
    Se envía de Reino Unido a Estados Unidos de America

    Cantidad disponible: Más de 20 disponibles

    PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798185504710

    Serie: Libro 2 de 7 - Data Science Foundations Series

    • Tapa blanda
    • Impresión bajo demanda

    Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 23,30

    Envío por EUR 44,07 
    Se envía de Reino Unido a Estados Unidos de America

    Cantidad disponible: 1 disponible

    Paperback. Condición: new. Paperback. Build a neural network that recognizes images and understands text - starting from a single neuron. Deep learning has a reputation for being impenetrable: the domain of specialists fluent in calculus most readers don't have. Deep Learning for Absolute Beginners proves that reputation wrong. Every idea - a neuron, a layer, backpropagation, a loss function - is built up from plain language and a concrete analogy, then made real with runnable Python and TensorFlow/Keras code, before any formula appears. This isn't a theory book. It's a build-it-yourself book. What You'll Actually BuildYour first working neural network, trained and evaluated in KerasA real image classifier using Convolutional Neural Networks (CNNs)A transfer learning project using a pretrained modelSequential models - RNNs, LSTMs, and GRUs - for time-dependent dataA natural language processing pipeline, from raw text to predictionsA working introduction to Transformers and attention - the architecture behind modern AIA complete, end-to-end deep learning project, entirely your own Why This Book Is DifferentBackpropagation and gradient descent, genuinely explained - not waved at, not skipped, actually understood.An honest comparison with classical machine learning. You'll learn exactly when a neural network is the right tool - and when a random forest still wins.Regularization gets a full chapter, so your models generalize instead of memorizing.Computer vision and NLP both covered in depth - not just one, with the other as an afterthought.Worked solutions for every exercise, across all sixteen chapters.Who This Book Is For Written for readers who already have working comfort with Python, NumPy, pandas, and core machine learning vocabulary - training and test sets, overfitting, evaluation metrics (Book 1 of this series, Machine Learning for Absolute Beginners, builds exactly this foundation if you're starting fresh). If you can train and evaluate a scikit-learn model, you're ready for this book. You do not need prior exposure to neural networks, calculus beyond an intuitive sense of a derivative, or advanced linear algebra. Every mathematical idea is grounded in working code before it's ever discussed abstractly. What You'll Be Able to Do By the final chapter, you'll be able to design a neural network architecture for a given problem, train it without it overfitting, debug a model that refuses to learn, build genuine image classifiers and text models, and understand - concretely, not just by reputation - how today's AI systems actually work underneath. Deep learning is not magic, and it is not only for specialists. It's a buildable skill. This book builds it, one carefully explained layer at a time. Scroll up and start training your first neural network today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…