Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks.

Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text,and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.

• Learn how to deploy deep learning models to production
• Explore PyTorch use cases from several leading companies
• Learn how to apply transfer learning to images
• Apply cutting-edge NLP techniques using a model trained on Wikipedia
• Use PyTorch’s torchaudio library to classify audio data with a convolutional-based model
• Debug PyTorch models using TensorBoard and flame graphs

• Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud

迈出掌握深度学习的下一步，机器学习方法正在一秒钟内改变我们周围的世界。在这本实用的书中，你将掌握使用Facebook开源的PyTorch框架的关键思想，并获得创建你自己的神经网络所需的最新技能。

伊恩-波因特向你展示了如何在基于云的环境中设置PyTorch，然后通过对每个元素的深入研究，引导你创建神经架构，以促进对图像、声音、文本等的操作。他还涵盖了将迁移学习应用于图像、调试模型和生产中的PyTorch等关键概念。

- 了解如何将深度学习模型部署到生产中

- 探索几个领先公司的PyTorch使用案例

- 学习如何将迁移学习应用于图像

- 使用在维基百科上训练的模型应用尖端的NLP技术

- 使用PyTorch的torchaudio库，用基于卷积的模型对音频数据进行分类

- 使用TensorBoard和火焰图对PyTorch模型进行调试

- 在谷歌云上运行的Docker容器和Kubernetes集群中部署生产中的PyTorch应用程序