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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 - Softcover
Inhaltsangabe
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.
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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
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Paperback. Zustand: new. Paperback. Practical Keras 3 for Deep LearningLearn 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 environmentUnderstand how neural networks learn from dataBuild models with the Sequential and Functional APIsPrepare, normalize, and split datasets correctlyConfigure losses, optimizers, metrics, and model trainingRecognize underfitting and overfittingImprove model generalization with practical techniquesBuild convolutional neural networks for image classificationUse pretrained models and transfer learningFine-tune models for better performanceCreate models with multiple inputs and outputsRun Keras workflows with TensorFlow, JAX, and PyTorch backendsSave trained models and load them for future predictionsBuild a complete deep learning project from problem definition to predictionEach 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798193623816
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