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PyTorch. Build, Evaluate, and Deploy Deep Learning Models Rheinwerk Publishing, Inc, Bert Gollnick

Książka w języku angielskim angielski
PyTorch. Build, Evaluate, and Deploy Deep Learning Models Rheinwerk Publishing, Inc, Bert Gollnick - okladka książki

PyTorch. Build, Evaluate, and Deploy Deep Learning Models Rheinwerk Publishing, Inc, Bert Gollnick - okladka książki

PyTorch. Build, Evaluate, and Deploy Deep Learning Models Rheinwerk Publishing, Inc, Bert Gollnick - audiobook MP3

PyTorch. Build, Evaluate, and Deploy Deep Learning Models Rheinwerk Publishing, Inc, Bert Gollnick - audiobook CD

Autorzy:
Rheinwerk Publishing, Inc, Bert Gollnick
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Bądź pierwszym, który oceni tę książkę
Stron:
419
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Deep learning concepts are introduced alongside the PyTorch workflow needed to turn them into working models. Readers begin with model creation and progress through regression and classification, gaining the theoretical context required to understand evaluation tools such as confusion matrices and ROC curves.

The middle of the journey expands into computer vision, recommendation systems, autoencoders, graph neural networks, time series forecasting, and language models. Hands-on exercises show how to create datasets, train networks, process sequential data, and generate images, while pretrained networks, Hugging Face fine-tuning, and PyTorch Lightning broaden the options for efficient development.

The closing material focuses on training visibility and production use. MLflow and TensorBoard support logging, metric review, and monitoring, while FastAPI and Heroku illustrate deployment on local infrastructure or in the cloud. By the end of this journey, readers can build, tune, evaluate, and deploy PyTorch models across a wide range of practical deep learning tasks.

O autorze książki

Bert Gollnick is a senior data scientist who specializes in renewable energies. For many years, he has taught courses about data science and machine learning, and more recently, about generative AI and natural language processing. Bert studied aeronautics at the Technical University of Berlin and economics at the University of Hagen. His main areas of interest are machine learning and data science.

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