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PyTorch and Deep Learning for Decision Makers (LFS116) Course Overview

PyTorch and Deep Learning for Decision Makers (LFS116) Course Overview

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.

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Course Prerequisites

Prerequisites for PyTorch and Deep Learning for Decision Makers (LFS116)

To ensure a successful learning experience in the PyTorch and Deep Learning for Decision Makers (LFS116) course, we recommend that participants have the following minimum knowledge and skills:


  • Basic Understanding of Python: Familiarity with Python programming, including basic syntax and data structures, will help you navigate the course materials effectively.
  • Introductory Statistics and Mathematics: A foundational grasp of statistics (mean, median, variance) and basic mathematical concepts (such as linear algebra) will facilitate your understanding of the algorithms discussed in the course.
  • Curiosity and Willingness to Learn: A genuine interest in artificial intelligence and machine learning will enhance your learning experience and engagement with the course content.

These prerequisites are designed to provide you with the essential tools to succeed in the course, while still being accessible to those new to the field.


PyTorch Essentials: An Applications-First Approach (LFD273)
PyTorch and Deep Learning for Decision Makers (LFS116)
Ethical Principles for Conversational AI (LFS118)

Target Audience for PyTorch and Deep Learning for Decision Makers (LFS116)

The PyTorch and Deep Learning for Decision Makers (LFS116) course equips professionals with essential skills in AI and deep learning to enhance data-driven decision-making in various industries.


  • Data Scientists
  • Machine Learning Engineers
  • Business Analysts
  • IT Managers
  • Software Developers
  • Product Managers
  • Researchers
  • Statisticians
  • Data Engineers
  • System Architects
  • Operations Managers
  • Executives in Technology
  • Marketing Analysts
  • Finance Professionals
  • Healthcare Analysts
  • Supply Chain Managers


Learning Objectives - What you will Learn in this PyTorch and Deep Learning for Decision Makers (LFS116)?

Course Introduction

The PyTorch and Deep Learning for Decision Makers (LFS116) course equips participants with essential skills in deep learning using PyTorch, focusing on practical applications for data-driven decision-making in various sectors.

Learning Objectives and Outcomes

  • Understand the fundamentals of deep learning and its relevance in decision-making contexts.
  • Gain proficiency in using the PyTorch framework for building and training neural networks.
  • Explore key deep learning concepts such as neural networks, loss functions, and optimization.
  • Learn to preprocess and manage data for effective machine learning model training.
  • Implement and evaluate different deep learning architectures for diverse applications.
  • Develop skills to deploy trained models for real-world decision-making scenarios.
  • Understand best practices for model monitoring and maintenance post-deployment.
  • Analyze case studies to leverage deep learning in real-world business challenges.
  • Acquire knowledge of ethical considerations and biases in AI-driven decisions.
  • Collaborate on projects that enhance practical understanding through hands-on experience.

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