Practical Keras 3 for Deep Learning: A Step-by-Step Beginner’s Guide to Python, Neural Networks, Model Training, TensorFlow, JAX, PyTorch, and Building Real-World AI Models

Kalman, Vincze

ISBN 13: 9798193623816

Edité par Independently published, 2026

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Détails bibliographiques

• Titre: Practical Keras 3 for Deep Learning: A Step-by-Step Beginner?s Guide to Python, Neural Networks, Model Training, TensorFlow, JAX, PyTorch, and Building Real-World AI Models
• Éditeur: Independently published
• Date de parution: 2026
• Langue: anglais
• ISBN à 13 chiffres: 9798193623816
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

Practical Keras 3 for Deep Learning

Learn deep learning by building real models with Keras 3, one practical step at a time.

Deep learning can feel overwhelming when you are faced with neural networks, training algorithms, multiple frameworks, and unfamiliar terminology all at once. Practical Keras 3 for Deep Learning gives you a clear, beginner-friendly path from your first Keras model to complete deep learning workflows you can understand, train, evaluate, improve, and reuse.

Rather than treating TensorFlow, JAX, and PyTorch as three separate technologies you must master, this book teaches you how to work primarily through Keras 3's modern multi-backend API while understanding how each backend fits into the process.

You will begin with the foundations, including Python setup, neural network inputs and outputs, weights, biases, layers, activation functions, loss, and optimization. From there, you will gradually move into practical model building, data preparation, training, evaluation, regularization, and prediction.

Inside this hands-on guide, you will learn how to:

• Set up Python, Keras 3, and a working deep learning environment

• Understand how neural networks learn from data

• Build models with the Sequential and Functional APIs

• Prepare, normalize, and split datasets correctly

• Configure losses, optimizers, metrics, and model training

• Recognize underfitting and overfitting

• Improve model generalization with practical techniques

• Build convolutional neural networks for image classification

• Use pretrained models and transfer learning

• Fine-tune models for better performance

• Create models with multiple inputs and outputs

• Run Keras workflows with TensorFlow, JAX, and PyTorch backends

• Save trained models and load them for future predictions

• Build a complete deep learning project from problem definition to prediction

Each chapter builds naturally on the previous one, with practical Python code and explanations focused on helping you understand not only what to write, but why each part of the workflow matters.

No previous experience with neural networks, TensorFlow, JAX, or PyTorch is required. If you know the basics of Python and want a structured introduction to modern deep learning, this book gives you the foundation you need to start building confidently with Keras 3.

Build the model. Train it. Evaluate it. Improve it. Then turn what you learned into real predictions.