PyTorch and Deep Learning for Decision Makers from edX

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.

LFS116x provides an overview of the AI landscape, focusing on PyTorch’s ecosystem, while giving you a solid understanding of AI’s current capabilities and it will help you make informed decisions about the development and maintenance of AI projects while taking in consideration key aspects related to data quality, model performance, and security.

What's inside

Learning objectives

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

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

Syllabus

Welcome to LFS116x!

Chapter 1. Why PyTorch? AI Applications in the Real World

Chapter 2. Take Good C.A.R.E. of Your Data

Chapter 3. "All Models are Wrong, but Some are Useful"

Chapter 4. Challenges in Deploying and Maintaining Applications

Final Exam (Verified Track only)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to PyTorch, a prominent deep learning framework utilized in industry

Suitable for learners from various backgrounds, providing an overview of AI and PyTorch's ecosystem

Highlights the importance of data quality for successful AI applications

Explores trade-offs involved in choosing appropriate models for specific tasks

Examines challenges and risks associated with AI deployment, including model limitations, biases, and data privacy

Delivered by instructors with expertise in the field of AI and deep learning

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in PyTorch and Deep Learning for Decision Makers with these activities:

Read 'Deep Learning with PyTorch' by Manning Publications

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Expand your understanding of PyTorch and its applications by reading a comprehensive book that covers its core concepts and real-world examples.

View Deep Learning with PyTorch: Build, train, and... on Amazon

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Read through the chapters sequentially
Take notes and highlight important sections
Complete the exercises and hands-on projects

Summarize PyTorch Concepts

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Create a concise summary or infographic explaining key PyTorch concepts to reinforce your learning and deepen your understanding.

Browse courses on Deep Learning

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Identify and list down fundamental PyTorch concepts
Summarize each concept using clear and concise language
Create visual aids or diagrams to illustrate the concepts

Explore the PyTorch Ecosystem

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Explore various libraries and resources available within the PyTorch ecosystem to enhance your understanding of its capabilities and applications.

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Review official PyTorch documentation and tutorials
Investigate popular libraries for computer vision, natural language processing, and more
Join online communities and forums to connect with other PyTorch users

Five other activities

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Join a PyTorch Study Group

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Collaborate with peers to discuss PyTorch concepts, share knowledge, and work through challenges together.

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Find or create a study group with other individuals interested in PyTorch
Set regular meeting times and establish a study schedule
Take turns presenting topics and facilitating discussions

Attend a PyTorch Workshop

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Enhance your skills and knowledge by attending a specialized PyTorch workshop led by industry experts.

Browse courses on Deep Learning

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Research and identify relevant PyTorch workshops
Register and participate in the workshop
Actively engage in discussions and hands-on exercises

Develop a Deep Learning Model in PyTorch

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Develop a custom deep learning model using PyTorch to reinforce your understanding of model development and implementation.

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Define the model architecture using PyTorch modules
Load and prepare the training data
Train the model using an appropriate optimizer and loss function
Evaluate the model's performance using metrics relevant to your task

Build a Deep Learning Project with PyTorch

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Apply your PyTorch skills by building a project that addresses a specific problem or use case.

Browse courses on Deep Learning

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Define the problem or use case you want to address
Gather and prepare the necessary data
Design and implement the deep learning model using PyTorch
Evaluate the model's performance and make any necessary adjustments

Optimize a Deep Learning Model

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Engage in hands-on optimization techniques to improve the performance and efficiency of your deep learning models.

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Identify potential areas for optimization, such as hyperparameter tuning or model architecture
Implement optimization algorithms and techniques
Evaluate the impact of optimization on model performance and efficiency

Career center

Learners who complete PyTorch and Deep Learning for Decision Makers will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists build models that generate insights from data gathered in vast quantities from an ever-growing collection of sources. The PyTorch and Deep Learning for Decision Makers online short course from The Linux Foundation can enhance your skill set and make you more desirable to a wider range of employers in this field. Most employers seek candidates with at least a master's degree in computer science, statistics, or a related discipline. Data Scientists should expect to work with cross-functional teams to advance business initiatives using data-driven solutions.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

The PyTorch and Deep Learning for Decision Makers course offered by The Linux Foundation can introduce you to the PyTorch framework and help you become a better Machine Learning Engineer. Among your job activities, you will apply machine learning to develop solutions across industries, and improve the efficiency and effectiveness of business processes. In this field, a master's degree is usually the minimum educational requirement.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers use their analytical skills to design, develop, test, and maintain software applications. The curriculum in the PyTorch and Deep Learning for Decision Makers online course provides a foundation for understanding the PyTorch framework and its use in the development and deployment of AI applications. This course may be particularly useful if you desire to specialize in the development of AI software.

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Quantitative Analyst

Quantitative Analysts use their modeling and data analysis skills to solve quantitative problems in finance, especially in the trading and risk management areas. If this is your career aspiration, taking the PyTorch and Deep Learning for Decision Makers course can be helpful because it can provide you with a deeper understanding of the use of PyTorch in data-related tasks. Employers in this field typically require a master's degree in a quantitative field and strong programming skills.

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Operations Research Analyst

Operations Research Analysts use analytic techniques to develop mathematical models to help organizations improve their efficiency. The PyTorch and Deep Learning for Decision Makers online short course can help you refine the skills companies in this field seek, which include model building and problem-solving skills. A master's degree in operations research, management science, industrial engineering, or a related discipline is typically required.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

Business Analysts use their business knowledge and analytical skills to identify and solve business problems. They may also design and implement solutions that improve efficiency and effectiveness. The PyTorch and Deep Learning for Decision Makers course can bolster your resume and make you more competitive in the job market if your career goal is to become a Business Analyst. This course will help you understand how to use PyTorch to solve business problems and make data-driven decisions.

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Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They use this information to make recommendations and solve business problems. The PyTorch and Deep Learning for Decision Makers online course can help you become more proficient in data analysis and make you more attractive to potential employers. Many employers seek candidates with a bachelor's degree in a related field, such as computer science, statistics, or mathematics.

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Statistician

Statisticians use statistical methods to collect, analyze, interpret, and present data. They work in a variety of industries, including finance, healthcare, and research. The PyTorch and Deep Learning for Decision Makers course can be useful for aspiring Statisticians because it provides an introduction to PyTorch and its use in data analysis. A master's degree in statistics or a related field is usually required for this role.

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Financial Analyst

Financial Analysts use their knowledge of finance and economics to analyze financial data and make investment recommendations. The PyTorch and Deep Learning for Decision Makers course can be beneficial for aspiring Financial Analysts because it can help them develop the skills needed to use PyTorch for financial data analysis. A bachelor's degree in finance or economics is typically required for this role.

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Actuary

Actuaries use their mathematical and statistical skills to assess and manage risk. They work in a variety of industries, including insurance, finance, and healthcare. The PyTorch and Deep Learning for Decision Makers course may be of interest to aspiring Actuaries because it can help them develop the skills needed to use PyTorch for risk assessment and management. A bachelor's degree in mathematics, statistics, or a related field is typically required for this role.

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Risk Manager

Risk Managers identify, assess, and manage risks. They work in a variety of industries, including finance, healthcare, and government. The PyTorch and Deep Learning for Decision Makers course may be of interest to aspiring Risk Managers because it can help them develop the skills needed to use PyTorch for risk assessment and management. A bachelor's degree in finance, risk management, or a related field is typically required for this role.

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Compliance Officer

Compliance Officers ensure that organizations comply with laws and regulations. They work in a variety of industries, including finance, healthcare, and government. The PyTorch and Deep Learning for Decision Makers course may be of interest to aspiring Compliance Officers because it can help them develop the skills needed to use PyTorch for compliance monitoring and reporting. A bachelor's degree in business, law, or a related field is typically required for this role.

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Fraud Analyst

Fraud Analysts investigate and prevent fraud. They work in a variety of industries, including finance, insurance, and government. The PyTorch and Deep Learning for Decision Makers course may be of interest to aspiring Fraud Analysts because it can help them develop the skills needed to use PyTorch for fraud detection and prevention. A bachelor's degree in finance, accounting, or a related field is typically required for this role.

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Auditor

Auditors examine and evaluate financial records to ensure accuracy and compliance with laws and regulations. They work in a variety of industries, including accounting, finance, and government. The PyTorch and Deep Learning for Decision Makers course may be of interest to aspiring Auditors because it can help them develop the skills needed to use PyTorch for data analysis and auditing. A bachelor's degree in accounting or a related field is typically required for this role.

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Consultant

Consultants provide advice and guidance to organizations on a variety of topics, including business strategy, operations, and technology. The PyTorch and Deep Learning for Decision Makers course may be of interest to aspiring Consultants because it can help them develop the skills needed to use PyTorch for data analysis and decision-making. A bachelor's degree in business, economics, or a related field is typically required for this role.

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