Build deep learning models with PyTorch for vision, recommendations, time series, and language tasks. Train, evaluate, fine-tune, monitor, and deploy models with modern supporting tools.
Key Features
- Broad model coverage spans vision, recommendations, graphs, forecasting, and language tasks
- Production-focused workflows combine training metrics, monitoring, fine-tuning, and deployment
- Modern PyTorch tooling includes Lightning, TensorBoard, MLflow, FastAPI, and Hugging Face tools
Book Description
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.
What you will learn
- Build neural networks with PyTorch
- Train regression and classification models
- Create computer vision and recommendation systems
- Develop autoencoders and graph neural networks
- Forecast time series and process language data
- Evaluate, monitor, and deploy trained models
Who this book is for
Ideal for developers, machine learning engineers, data scientists, and research scientists who want practical PyTorch experience. Readers will benefit from an interest in building, evaluating, and deploying deep learning models across several application areas.
