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Joseph Santarcangelo
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
Read more
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
Read more
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
In this capstone project, you'lluse a Deep Learning library ofyour choice to develop, train, and test a Deep Learning model.Loadand preprocess data for a real problem, build the model and then validate it.
Finally, you will present a project report to demonstrate the validity of yourmodel andyour proficiency in the field of deep learning.
What you'll learn
- Determine what kind of Deep Learning method to use in which situation
- Know how to build a Deep Learning model to solve a real problem
- Master the process of creating a Deep Learning pipeline
- Apply knowledge of Deep Learning to improve models using real data
- Demonstrate ability to present and communicate outcomes of Deep Learning projects
Register for this course and see more details by visiting:
OpenCourser.com/course/j3ufc6/applied
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What's inside
Learning objectives
- Determine what kind of deep learning method to use in which situation
- Know how to build a deep learning model to solve a real problem
- Master the process of creating a deep learning pipeline
- Apply knowledge of deep learning to improve models using real data
- Demonstrate ability to present and communicate outcomes of deep learning projects
- Determine what kind of deep learning method to use in which situation
- Know how to build a deep learning model to solve a real problem
- Master the process of creating a deep learning pipeline
- Apply knowledge of deep learning to improve models using real data
- Demonstrate ability to present and communicate outcomes of deep learning projects
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Offers a practical capstone experience to apply deep learning theory to real-world problems
Taught by Joseph Santarcangelo, an experienced instructor with industry expertise
Provides hands-on experience in developing, training, and validating deep learning models
Guides learners through the process of creating a deep learning pipeline
Develops skills in applying deep learning to improve models using real data
May require prior experience in deep learning theory
Save this course
Save Applied Deep Learning Capstone Project to your list so you can find it easily later:
Save
Save
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 Applied Deep Learning Capstone Project with these
activities:
Review 'Deep Learning' by Goodfellow, Bengio, and Courville
Show steps
Reinforce understanding of Deep Learning concepts and familiarize with a comprehensive reference book.
View
Deep Learning
on Amazon
Show steps
-
Read chapters relevant to the course content.
-
Work through exercises and examples provided in the book.
-
Summarize key concepts and techniques.
Revise Calculus
Show steps
Review the basics of calculus to confirm a strong foundation for successful completion of this course.
Browse courses on
Calculus
Show steps
-
Review notes and materials from previous Calculus courses.
-
Practice solving basic Calculus problems.
-
Online tutorial on Derivatives and Integrals.
Follow Guided Tutorials
Show steps
Expand knowledge of Deep Learning by exploring additional resources and refining skills.
Show steps
-
Watch video tutorials on Deep Learning concepts and techniques.
-
Read articles and blog posts on recent advancements in Deep Learning.
-
Complete online courses or workshops on specific Deep Learning topics.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice Deep Learning Code
Show steps
Improve coding skills and reinforce understanding of Deep Learning algorithms.
Show steps
-
Solve coding challenges on platforms like LeetCode or HackerRank.
-
Implement various Deep Learning algorithms from scratch.
-
Participate in online coding competitions.
Join a Deep Learning Study Group
Show steps
Collaborate with peers to discuss concepts, share resources, and provide mutual support.
Show steps
-
Find a study group or create one with classmates.
-
Meet regularly to discuss course materials and work on projects.
-
Share knowledge and support each other in the learning process.
Develop a Deep Learning Project
Show steps
Demonstrate proficiency in Deep Learning by building a project that solves a real-world problem.
Show steps
-
Identify a problem that can be addressed using Deep Learning.
-
Gather and preprocess data for the project.
-
Design and implement a Deep Learning model to solve the problem.
Attend Deep Learning Meetups and Conferences
Show steps
Connect with other Deep Learning professionals, learn about new trends, and expand your network.
Show steps
-
Identify relevant meetups or conferences in your area.
-
Attend sessions, workshops, and networking events.
-
Exchange ideas, share insights, and build connections.
Start a Deep Learning Research Project
Show steps
Develop independent research skills and explore advanced Deep Learning topics.
Show steps
-
Identify a research topic of interest.
-
Review existing literature and identify research gaps.
-
Design and implement a research plan.
Mentor Junior Deep Learning Learners
Show steps
Share knowledge and help others develop their Deep Learning skills.
Show steps
-
Volunteer as a mentor for beginners or students in lower-level courses.
-
Provide guidance, support, and encouragement.
-
Review and provide feedback on their work.
Review 'Deep Learning' by Goodfellow, Bengio, and Courville
Show steps
Reinforce understanding of Deep Learning concepts and familiarize with a comprehensive reference book.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read chapters relevant to the course content.
- Work through exercises and examples provided in the book.
- Summarize key concepts and techniques.
Revise Calculus
Show steps
Review the basics of calculus to confirm a strong foundation for successful completion of this course.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review notes and materials from previous Calculus courses.
- Practice solving basic Calculus problems.
- Online tutorial on Derivatives and Integrals.
Follow Guided Tutorials
Show steps
Expand knowledge of Deep Learning by exploring additional resources and refining skills.
Show steps
Show steps
Show steps
- Watch video tutorials on Deep Learning concepts and techniques.
- Read articles and blog posts on recent advancements in Deep Learning.
- Complete online courses or workshops on specific Deep Learning topics.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice Deep Learning Code
Show steps
Improve coding skills and reinforce understanding of Deep Learning algorithms.
Show steps
Show steps
Show steps
- Solve coding challenges on platforms like LeetCode or HackerRank.
- Implement various Deep Learning algorithms from scratch.
- Participate in online coding competitions.
Join a Deep Learning Study Group
Show steps
Collaborate with peers to discuss concepts, share resources, and provide mutual support.
Show steps
Show steps
Show steps
- Find a study group or create one with classmates.
- Meet regularly to discuss course materials and work on projects.
- Share knowledge and support each other in the learning process.
Develop a Deep Learning Project
Show steps
Demonstrate proficiency in Deep Learning by building a project that solves a real-world problem.
Show steps
Show steps
Show steps
- Identify a problem that can be addressed using Deep Learning.
- Gather and preprocess data for the project.
- Design and implement a Deep Learning model to solve the problem.
Attend Deep Learning Meetups and Conferences
Show steps
Connect with other Deep Learning professionals, learn about new trends, and expand your network.
Show steps
Show steps
Show steps
- Identify relevant meetups or conferences in your area.
- Attend sessions, workshops, and networking events.
- Exchange ideas, share insights, and build connections.
Start a Deep Learning Research Project
Show steps
Develop independent research skills and explore advanced Deep Learning topics.
Show steps
Show steps
Show steps
- Identify a research topic of interest.
- Review existing literature and identify research gaps.
- Design and implement a research plan.
Mentor Junior Deep Learning Learners
Show steps
Share knowledge and help others develop their Deep Learning skills.
Show steps
Show steps
Show steps
- Volunteer as a mentor for beginners or students in lower-level courses.
- Provide guidance, support, and encouragement.
- Review and provide feedback on their work.
Career center
Learners who complete Applied Deep Learning Capstone Project will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
A Deep Learning Engineer focuses on developing and deploying Deep Learning models. Deep Learning models are powerful tools that can be used to solve a variety of problems, such as image recognition, natural language processing, and speech recognition. This course teaches you how to build Deep Learning models, which is a valuable skill for aspiring Deep Learning Engineers. Additionally, this course can help you build a solid foundation in Deep Learning that can be useful for developing new Deep Learning algorithms and architectures.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods to extract knowledge from data in order to help make informed decisions. A key part of a Data Scientist's role is building Deep Learning pipelines to handle big data. This course teaches you how to build a Deep Learning pipeline, which is an essential skill for a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys Machine Learning models to automate tasks and improve efficiency. This course focuses on teaching the skills needed to build Deep Learning models. Deep Learning models use advanced algorithms to process large amounts of data and can be used to perform a variety of tasks, such as image recognition and natural language processing. As a result, this course can help you develop the skills needed to become a Machine Learning Engineer.
Research Scientist
Research Scientist
A Research Scientist conducts research to develop new technologies and solutions. This course teaches you how to build Deep Learning models, which are powerful tools that can be used to solve a variety of problems. As a result, this course can help you develop the skills and knowledge needed to become a Research Scientist who can use Deep Learning to make new discoveries and develop new technologies.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data to help businesses make better decisions. This course teaches you how to build Deep Learning models, which can be used to analyze large amounts of data and identify trends and patterns. As a result, this course can help you develop the skills needed to become a Business Intelligence Analyst who can use Deep Learning to gain a competitive advantage for your business.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course teaches you how to build Deep Learning models, which can be used to improve the performance of software applications. As a result, this course can help you develop the skills needed to become a Software Engineer who can develop cutting-edge software applications that use Deep Learning.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help organizations make informed decisions. This course teaches you how to build Deep Learning models, which can be used to analyze large amounts of data and identify trends and patterns. As a result, this course can help you develop the skills needed to become a Data Analyst who can use Deep Learning to extract valuable insights from data.
Fraud Analyst
Fraud Analyst
A Fraud Analyst investigates and prevents fraud. This course teaches you how to build Deep Learning models, which can be used to detect and prevent fraud. As a result, this course can help you develop the skills needed to become a Fraud Analyst who can use Deep Learning to protect organizations from financial loss.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to improve the efficiency of operations. This course teaches you how to build Deep Learning models, which can be used to optimize operations. As a result, this course can help you develop the skills needed to become an Operations Research Analyst who can use Deep Learning to improve the efficiency of organizations.
Risk Analyst
Risk Analyst
A Risk Analyst identifies and assesses risks to help organizations make informed decisions. This course teaches you how to build Deep Learning models, which can be used to analyze large amounts of data and identify risks. As a result, this course can help you develop the skills needed to become a Risk Analyst who can use Deep Learning to help organizations mitigate risks.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. This course teaches you how to build Deep Learning models, which can be used to improve the performance of products. As a result, this course can help you develop the skills needed to become a Product Manager who can develop innovative products that use Deep Learning to meet the needs of customers.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course teaches you how to build Deep Learning models, which are powerful tools that can be used to analyze large amounts of data and identify trends and patterns. As a result, this course can help you develop the skills needed to become a Quantitative Analyst who can use Deep Learning to make better investment decisions.
Cybersecurity Analyst
Cybersecurity Analyst
A Cybersecurity Analyst protects computer systems and networks from cyberattacks. This course teaches you how to build Deep Learning models, which can be used to detect and prevent cyberattacks. As a result, this course can help you develop the skills needed to become a Cybersecurity Analyst who can use Deep Learning to keep organizations safe from cyber threats.
Management Consultant
Management Consultant
A Management Consultant advises organizations on how to improve their performance. This course teaches you how to build Deep Learning models, which can be used to analyze data and identify opportunities for improvement. As a result, this course can help you develop the skills needed to become a Management Consultant who can use Deep Learning to help organizations achieve their goals.
Data Architect
Data Architect
A Data Architect designs and builds data systems. This course teaches you how to build Deep Learning models, which can be used to process and analyze large amounts of data. As a result, this course may be useful for aspiring Data Architects who want to learn how to use Deep Learning to build more efficient and effective data systems.
For more career information including salaries, visit:
OpenCourser.com/course/j3ufc6/applied
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Applied Deep Learning Capstone Project.
Save
Provides a comprehensive overview of deep learning, covering the theoretical foundations, algorithms, and applications of deep learning models. It valuable resource for anyone who wants to learn more about deep learning or apply it to real-world problems.
Provides a comprehensive overview of neural networks and deep learning, covering the theoretical foundations, algorithms, and applications of neural networks. It valuable resource for anyone who wants to learn more about neural networks or apply them to real-world problems.
Provides a comprehensive overview of deep learning for natural language processing, covering the theoretical foundations, algorithms, and applications of deep learning models for NLP tasks. It valuable resource for anyone who wants to learn more about deep learning for NLP or apply it to real-world problems.
Provides a comprehensive overview of deep learning for finance, covering the theoretical foundations, algorithms, and applications of deep learning models for finance tasks. It valuable resource for anyone who wants to learn more about deep learning for finance or apply it to real-world problems.
Provides a comprehensive overview of deep learning for transportation, covering the theoretical foundations, algorithms, and applications of deep learning models for transportation tasks. It valuable resource for anyone who wants to learn more about deep learning for transportation or apply it to real-world problems.
Provides a comprehensive overview of deep learning for telecommunications, covering the theoretical foundations, algorithms, and applications of deep learning models for telecommunications tasks. It valuable resource for anyone who wants to learn more about deep learning for telecommunications or apply it to real-world problems.
Provides a comprehensive overview of deep learning for cybersecurity, covering the theoretical foundations, algorithms, and applications of deep learning models for cybersecurity tasks. It valuable resource for anyone who wants to learn more about deep learning for cybersecurity or apply it to real-world problems.
Provides a practical introduction to machine learning, using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It good resource for anyone who wants to learn how to build and train machine learning models.
Provides a practical introduction to deep learning, using the Python programming language. It good resource for anyone who wants to learn how to build and train deep learning models using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/j3ufc6/applied
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Effort
2 to 4 hours per week for 5 weeks
Level
Introductory
Via
edX
Institution
IBM
Instructor
Joseph Santarcangelo
Language
English
Transcript
English
This course belongs to a
collection
called
Deep Learning. It's comprised of seven courses:
Good to know
Offers a practical capstone experience to apply deep learning theory to real-world problems
Taught by Joseph Santarcangelo, an experienced instructor with industry expertise
Provides hands-on experience in developing, training, and validating deep learning models
Guides learners through the process of creating a deep learning pipeline
Develops skills in applying deep learning to improve models using real data
May require prior experience in deep learning theory
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