This is the code repository for Dynamic Neural Network Programming with PyTorch [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Deep learning influences key aspects of core sectors such as IT, finance, science, and many more. Problems arise when it comes to getting computational resources for your network. You need to have a powerful GPU and plenty of time to train a network for solving a real-world task. Dynamic neural networks help save training time on your networks. They also reduce the amount of computational resources required. In this course, you'll learn to combine various techniques into a common framework. Then you will use dynamic graph computations to reduce the time spent training a network. By the end, you'll be ready to use the power of PyTorch to easily train neural networks of varying complexities.
- Connect, import, and transform data for business intelligence
- Create dashboards and real-time reports to share with business users on the web and on mobile
- Use natural language querying for data visualization
- Integrate Power BI with other tools, including Microsoft Excel to connect your Excel workbooks
- Share reports and dashboards based on the Power BI desktop
- Use customized charts from Power BI marketplace
- Explore the Power BI mobile app
- Apply predictive analytics to improve business decision-making
To fully benefit from the coverage included in this course, you will need:
This course is for developers who want to extend their skillset to Neural Networks using PyTorch.
This course will also appeal to someone who has a basic understanding of ML concepts and Python programming but now wants to learn how to implement them with PyTorch.
This course has the following software requirements:
Mac OS
1,7 GHz Intel Core i7
RAM: 8 GB 1600 MHz DDR3
Graphics: Intel HD Graphics 5000 1536 ะะ
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent