PyTorch and Deep Learning for Business Leaders
Learn how to leverage PyTorch's deep learning framework to develop and deploy AI applications for business optimization and automation.
Learn how to leverage PyTorch's deep learning framework to develop and deploy AI applications for business optimization and automation.
This comprehensive course introduces PyTorch, a leading deep learning framework, and its applications in business. You'll discover how PyTorch's ecosystem can automate and optimize processes through AI applications. The course covers essential topics including data quality importance, model selection criteria, and deployment challenges. Learn to identify AI use cases across industries, understand the trade-offs in model development, and address critical aspects of AI implementation including security, privacy, and performance optimization. Perfect for both technical and non-technical professionals seeking to drive business value through AI.
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What you'll learn
Evaluate AI applications across different industries
Understand PyTorch's role in modern AI development
Assess data quality impacts on model performance
Analyze trade-offs in AI model development and acquisition
Identify and mitigate AI deployment risks and challenges
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This course provides a comprehensive overview of PyTorch and deep learning for business decision-makers. It focuses on practical applications of AI in industry, covering essential aspects from data quality management to model deployment. Students learn to evaluate AI use cases, understand PyTorch's ecosystem, and make informed decisions about AI project implementation. The curriculum emphasizes real-world considerations including model selection criteria, security concerns, and privacy issues. Special attention is given to the business implications of AI deployment and the importance of data quality in successful AI applications.
Welcome to LFS116x
Module 1
Why PyTorch? AI Applications in the Real World
Module 2
Take Good C.A.R.E. of Your Data
Module 3
All Models are Wrong, but Some are Useful
Module 4
Challenges in Deploying and Maintaining Applications
Module 5
Final Exam
Module 6
Fee Structure
Instructor
1 Course
Data Scientist and Educator Advancing PyTorch and Deep Learning
Daniel Voigt Godoy is a multifaceted professional with over 20 years of experience across various industries, including banking, government, fintech, retail, and mobility. He is best known for his contributions to the field of data science, particularly his work with PyTorch and deep learning. Godoy is the author of the highly regarded "Deep Learning with PyTorch Step-by-Step" book series, which has gained recognition for its comprehensive and accessible approach to teaching PyTorch. As an instructor at Data Science Retreat in Berlin, he has helped more than 150 students advance their careers in machine learning and distributed computing technologies. Godoy is also a regular speaker at the Open Data Science Conference, where he conducts workshops on PyTorch and Generative Adversarial Networks for beginners. His expertise extends to developing open-source tools, as evidenced by his role as the main contributor to HandySpark, a Python package designed to facilitate data exploration using Apache Spark. Godoy's academic achievements include winning four consecutive awards at the prestigious Prêmio do Tesouro Nacional (Brazilian's National Treasury Award) from 2012 to 2015. His teaching philosophy emphasizes a structured, incremental, and first-principles approach to learning complex technical concepts, making him a respected educator in the data science community.
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