Deep Learning for Absolute Beginners (Paperback)
Idioma: inglés
Editorial: Independently Published, 2026
- Tapa blanda
- Nuevo
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Vendedor de IberLibro desde 29 de junio de 2022
Condición: Nuevo
EUR 23,30
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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.…
N° de ref. del artículo 9798185504710
- Título
- Deep Learning for Absolute Beginners (Paperback)
- Autor
- Garrett Hawthorne
- Editorial
- Independently Published
- Año de publicación
- 2026
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 13
- 9798185504710
- Serie
- Libro 2 de 6: Data Science Foundations Series
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 Build
- Your first working neural network, trained and evaluated in Keras
- A real image classifier using Convolutional Neural Networks (CNNs)
- A transfer learning project using a pretrained model
- Sequential models — RNNs, LSTMs, and GRUs — for time-dependent data
- A natural language processing pipeline, from raw text to predictions
- A working introduction to Transformers and attention — the architecture behind modern AI
- A complete, end-to-end deep learning project, entirely your own
- Backpropagation 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.
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.
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CitiRetail
Stevenage, Reino Unido
Vendedor de IberLibro desde 29 de junio de 2022
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