《Deep Learning with PyTorch》PDF电子书下载
《Deep Learning with PyTorch》内容简介
Eli Stevens、Luca Antiga / Manning Publications / 2020-6-9 / USD 49.99 -
Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be. After a quick introduction to the deep learning landscape, you'll explore the use of pre-trained networks and start sharpening your skills on working with tensors. You'll find out how to represent the most common types of data with tensors and how to build and train neural networks from scratch on practical examples, focusing on images and sequences.
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.
what's inside
Using the PyTorch tensor API
Understanding automatic differentiation in PyTorch
Training deep neural networks
Monitoring training and visualizing results
Implementing modules and loss functions
Loading data in Python for PyTorch
Interoperability with NumPy
Deploying a PyTorch model for inference
Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be. After a quick introduction to the deep learning landscape, you'll explore the use of pre-trained networks and start sharpening your skills on working with tensors. You'll find out how to represent the most common types of data with tensors and how to build and train neural networks from scratch on practical examples, focusing on images and sequences.
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.
what's inside
Using the PyTorch tensor API
Understanding automatic differentiation in PyTorch
Training deep neural networks
Monitoring training and visualizing results
Implementing modules and loss functions
Loading data in Python for PyTorch
Interoperability with NumPy
Deploying a PyTorch model for inference
同类热门电子书下载更多
- Theoretical Neuroscience
- 数字创世纪
- 计算物理学
- 神经网络、模糊系统及其在运动控制中的确应用
- 自由与神经生物学
- 大脑设计
- 模糊逻辑与神经网络:理论研究与探索
- Word排版艺术
- 智能控制技术
- Handbook of Exact String Matching Algorithms
- 神经—模糊和软计算
- Eye, Brain, and Vision
- 图神经网络
- Neural Networks and Learning Machines
- 人工智能产品经理：人机对话系统设计逻辑探究
- 模式识别与神经网络
- 神经计算原理
- Neural Network Methods in Natural Language Processing
- 神经网络理论与MATLAB7实现
- 神经网络设计（原书第2版）