Deep learning python hands on (43 Ergebnisse)

Paperback. Zustand: Fair. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

paperback. Zustand: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned.…

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Zustand: New. In English.

Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Zustand: As New. Like New condition. A near perfect copy that may have very minor cosmetic defects.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New.

Zustand: New. In English.

Zustand: New. In English.

Paperback. Zustand: Brand New. 450 pages. 9.50x7.00x1.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware - Can AI truly be trusted in the clinic In the high-stakes world of healthcare, 'close enough' isn't good enough.The gap between a Python script on a laptop and a deployed model in a hospital is a chasm filled with data silos, interoperability hurdles, and ethical minefields. Applied Clinical AI is the master blueprint for engineers and healthcare professionals who need to build, scale, and deploy AI that works in the real world.Authored by MOMENT TECH, this hands-on guide skips the hype and dives straight into the technical architecture required to modernize healthcare. From mastering the nuances of FHIR (Fast Healthcare Interoperability Resources) to fine-tuning LLMs for medical reasoning, this book provides the production-grade code and strategy needed for 2026 and beyond.Inside this comprehensive blueprint, you will master: The Modern Stack: Build robust clinical pipelines using Python, Transformers, and PyTorch.FHIR Integration: Bridge the gap between legacy EHR data and modern AI models with seamless interoperability.LLMs in Medicine: Go beyond generic prompts-learn to fine-tune Large Language Models for high-accuracy clinical decision support.Generative Healthcare: Implement VAEs, GANs, and Diffusion models for medical imaging and synthetic data generation.Production & Scale: Move from local training to distributed clusters with FSDP and high-performance deployment wrappers.The Ethics of Autonomy: Design agentic workflows that maintain 'Human-in-the-Loop' safety and HIPAA/GDPR compliance.Whether you are a senior software engineer moving into HealthTech or a clinical researcher looking to operationalize your models, this book is your technical North Star.Stop building prototypes. Start building the future of medicine.…

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. 2019. Paperback. . . . . . Books ship from the US and Ireland.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - Build, train, evaluate, and use your first neural networks with Keras 3, one practical step at a time.Deep learning can feel overwhelming when you are faced with unfamiliar terminology, mathematical explanations, and code that moves too quickly. Keras 3 Deep Learning Fundamentals gives beginners a clearer path.This hands-on guide teaches you how to work with Keras 3 and Python by focusing on the skills you actually need to understand and build practical neural-network models. Instead of treating Keras as a collection of commands to memorize, you will learn how the complete deep learning workflow fits together, from preparing data and building layers to training, evaluating, improving, and making predictions.You will work through a continuous Fashion-MNIST image classification project, allowing you to see how each concept contributes to a real model rather than jumping between unrelated examples.Inside the book, you will learn how to: - Set up Python, Keras 3, and a working deep learning environment- Understand neurons, weights, biases, layers, and activation functions- Prepare and scale data for neural-network training- Build models using Keras layers and the Sequential API- Choose loss functions, optimizers, and evaluation metrics- Train models with model.fit() and understand epochs and batches- Use validation data to monitor learning- Evaluate trained models on unseen data- Recognize underfitting, overfitting, and common performance problems- Improve models by adjusting network size, learning rates, and batch sizes- Use early stopping to control training- Make predictions with trained neural networks- Convert model outputs into useful classification results- Save, load, and reuse Keras models- Bring the complete workflow together in a finished Keras 3 projectThe book uses TensorFlow as the Keras backend so you can stay focused on learning Keras instead of switching between multiple frameworks. Advanced mathematics is kept to a minimum, while important concepts are explained clearly enough for you to understand what your code is doing and why.You do not need previous deep learning experience. If you already understand basic Python and want a practical introduction to neural networks, model training, and modern Keras development, this book will help you build a strong foundation.Stop treating deep learning as something you only read about. Start building models, training them, evaluating the results, and making real predictions with Keras 3.…

Zustand: New. 2019. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: Brand New. 68 pages. 8.66x5.91x0.16 inches. In Stock.