Ulteriori informazioni
PyTorch is the framework for deep learning-so dive on in! Learn how to train, optimize, and deploy AI models with PyTorch by following practical exercises and example code. You'll walk through using PyTorch for linear regression, classification, image processing, recommendation systems, autoencoders, graph neural networks, time series predictions, and language models-all the essentials. Then evaluate and deploy your models using key tools like MLflow, TensorBoard, and FastAPI. With information on fine-tuning your models using HuggingFace and reducing training time with PyTorch Lightning, this practical guide is the one you need!
Highlights:
1) Deep learning
2) Linear regression
3) Classification
4) Computer vision
5) Recommendation systems
6) Autoencoders
7) Graph neural networks (GNNs)
8) Time series predictions
9) Language models
10) Pretrained networks
11)Evaluation and deployment
12)PyTorch Lightning
Info autore
Bert Gollnick is a senior data scientist, specializing 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.
Riassunto
PyTorch is the framework for deep learning—so dive on in! Learn how to train, optimize, and deploy AI models with PyTorch by following practical exercises and example code. You’ll walk through using PyTorch for linear regression, classification, image processing, recommendation systems, autoencoders, graph neural networks, time series predictions, and language models—all the essentials. Then evaluate and deploy your models using key tools like MLflow, TensorBoard, and FastAPI. With information on fine-tuning your models using HuggingFace and reducing training time with PyTorch Lightning, this practical guide is the one you need!
Highlights:
1) Deep learning
2) Linear regression
3) Classification
4) Computer vision
5) Recommendation systems
6) Autoencoders
7) Graph neural networks (GNNs)
8) Time series predictions
9) Language models
10) Pretrained networks
11)Evaluation and deployment
12)PyTorch Lightning
