Deep learning with PyTorch

Eli Stevens ; Luca Antiga ; Thomas Viehmann ; Soumith Chintala

Editeur
Manning Publications Co.
Année de publication
2020
ISBN
978-1-61729-526-3

<mark>Deep Learning with PyTorch</mark> teaches you to create neural networks and <mark>deep learning</mark> systems <mark>with PyTorch</mark>. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the <mark>PyTorch</mark> Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning.</span>

puce  Accès à la notice sur le site du portail documentaire de Météo-France

  Liste complète des notices publiques