机读格式显示(MARC)
- 000 02089cam a2200337 i 4500
- 008 210712s2020 nyua b 001 0 eng
- 020 __ |a 9781617295263 : |c CNY357.93
- 040 __ |a DLC |b eng |c DLC |e rda |d DLC
- 050 00 |a QA76.87 |b .S745 2020
- 100 1_ |a Stevens, Eli, |e author.
- 245 10 |a Deep learning with PyTorch / |c Eli Stevens, Luca Antiga, and Thomas Viehmann ; foreword by Soumith Chintala.
- 264 _1 |a Shelter Island, NY : |b Manning Publications Co., |c [2020]
- 300 __ |a xxviii, 490 pages : |b illustrations ; |c 24 cm
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 504 __ |a Includes bibliographical references and index.
- 520 __ |a Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. 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 PyTorch 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.
- 650 _0 |a Neural networks (Computer science)
- 650 _0 |a Machine learning.
- 650 _0 |a Artificial intelligence.
- 650 _0 |a Python (Computer program language)
- 700 1_ |a Antiga, Luca, |e author.
- 700 1_ |a Viehmann, Thomas, |e author.