PyTorch and Deep Learning for Decision Makers
This course introduces you to PyTorch, one of the most popular deep learning frameworks, revealing how it can be used in your company to automate and optimize processes through the development and deployment of state-of-the-art AI applications. The course will help you identify the most common use cases of AI in the industry and how PyTorch’s ecosystem and the commoditization of deep learning models can help you integrate them into your business. You will also learn why ensuring data quality is critical for the successful deployment of AI applications, and why getting the right data should be the top priority for any AI project. The course will discuss several trade-offs involved in choosing the appropriate model for the task at hand: build vs. buy, black vs. white box, and the risk and cost of delivering wrong predictions.
Finally, the course will discuss what happens after an AI application is deployed, addressing topics such as the inherent limitations of AI models, the mitigation of risks and vulnerabilities, and the challenge of data privacy.
This course targets technical and non-technical individuals interested in understanding how deep learning and PyTorch can be used to create business value through the development and deployment of AI applications.
Upcoming start dates
- Self-paced Online
Who should attend?
- Why PyTorch? AI Applications in the Real World
- Take Good C.A.R.E. of Your Data
- "All Models are Wrong, but Some are Useful"
- Challenges in Deploying and Maintaining Applications
- Final Exam
Course delivery details
This course is offered through The Linux Foundation, a partner institute of EdX.
1-2 hours per week
- Verified Track -$149
- Audit Track - Free
Certification / Credits
What you'll learn
- Discuss typical use cases of AI across industries
- Understand the role and importance of PyTorch in the current AI landscape
- Discuss the importance of data quality and its overall impact on the performance of a model or application
- Understand the trade-offs involved in developing or buying a model or application
- Identify the challenges and risks involved in deploying an application: biases, attacks, privacy issues, and more
Contact this provider
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