Deep Learning for Absolute Beginners: Neural Networks from Scratch with Python and TensorFlow (Data Science Foundations Series) - Softcover

Inhaltsangabe

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 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

Why This Book Is Different

• 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.

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.

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