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Alex Aklson
and
Joseph Santarcangelo
In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/bmrveo/ai
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What's inside
Syllabus
Module 1 - Loading Data
In this module, you will get introduced to the problem that we will try to solve throughout the course. You will also learn how to load the image dataset, manipulate images, and visualize them.
Read more
Syllabus
Module 1 - Loading Data
In this module, you will get introduced to the problem that we will try to solve throughout the course. You will also learn how to load the image dataset, manipulate images, and visualize them.
Read more
Module 2
In this Module, you will mainly learn how to process image data and prepare it to build a classifier using pre-trained models.
Module 3
In this Module, in the PyTorch part, you will learn how to build a linear classifier. In the Keras part, you will learn how to build an image classifier using the ResNet50 pre-trained model.
Module 4
In this Module, in the PyTorch part, you will complete a peer review assessment where you will be asked to build an image classifier using the ResNet18 pre-trained model. In the Keras part, for the peer review assessment, you will be asked to build an image classifier using the VGG16 pre-trained model and compare its performance with the model that we built in the previous Module using the ResNet50 pre-trained model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners exploring deep learning and computer vision
Examines deep learning model development, an essential skill for data scientists and machine learning engineers
Develops critical thinking and problem-solving skills
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Reviews summary
Well-received ai capstone project
Learners say that this course is largely positive, providing engaging assignments, clear instruction, and hands-on experience with AI and deep learning. However, learners mention encountering some issues with the provided labs and resources, which can cause frustration and delays. The course offers two tracks, one using Keras and the other using PyTorch, giving learners a choice in their learning experience.
Learners have the choice between Keras and PyTorch tracks, allowing them to tailor the course to their preferences.
"The idea of choosing between Keras Track or PyTorch Track was very beautiful."
"I can suggest another track for TensorFlow, making it a choice between choosing from 3 tracks instead. That would feel more complete."
Learners appreciate the clear instruction and well-done lectures in the course.
"The teaching of this course is clear and complete"
"Course lecture are well done"
"Course is very good and easy to understand, Instructors have put in a lot of efforts to design the course"
The course provides learners with the opportunity to apply their learning through hands-on assignments and a capstone project.
"I got a chance to put what I learnt into practice"
"Very Good course. Learnt a lot from this course. Also got good hands-on experience."
Several learners experienced difficulties with the IBM Cognitive Labs environment, causing delays and frustrations.
"I had to create an account on AWS to get my model to run."
"The provided compiling environment is inexcusable. I tried five different emulators to get any type of results within six hours."
"IBM Cognitive Labs, the intended environment for the assignments, is incapable of running the later labs (week 3 + final)"
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 AI Capstone Project with Deep Learning with these
activities:
Review linear algebra
Show steps
Review the mathematical concepts that are fundamental to deep learning.
Browse courses on
Linear Algebra
Show steps
-
Review the concepts of vectors, matrices, and linear transformations.
-
Practice solving linear equations and systems of equations.
-
Review the concepts of eigenvalues and eigenvectors.
Read 'Deep Learning' by Goodfellow et al.
Show steps
Gain a comprehensive understanding of the foundations and applications of deep learning.
View
Deep Learning
on Amazon
Show steps
-
Read the book thoroughly.
-
Take notes and highlight key concepts.
-
Complete the exercises and assignments provided in the book.
Follow online tutorials on deep learning
Show steps
Supplement your classroom learning with additional resources and practical examples.
Show steps
-
Find reputable online tutorials on deep learning.
-
Follow the tutorials and complete the exercises.
-
Research additional resources to expand your understanding.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Exposing yourself to content not directly related to the course will expand your worldview on the subject of Deep Learning and help you approach the framework of Deep Learning from a broader, more comprehensive vantage point
View
Deep Learning
on Amazon
Show steps
-
Look over the table of contents
-
Read the first three chapters
-
Do the practice problems at the end of each chapter
Connect with deep learning experts
Show steps
Seek guidance and advice from experienced practitioners in the field.
Show steps
-
Identify potential mentors through online forums, conferences, or research papers.
-
Reach out to mentors and express your interest in their work.
-
Meet with mentors regularly to discuss your progress and seek advice.
Participate in peer review sessions
Show steps
Gain valuable feedback on your work and learn from the experiences of others.
Show steps
-
Attend peer review sessions.
-
Present your work to others.
-
Provide constructive feedback to your peers.
Complete coding exercises
Show steps
Sharpen your programming skills and apply your knowledge of deep learning algorithms.
Show steps
-
Solve coding exercises related to deep learning algorithms.
-
Practice implementing deep learning models using TensorFlow or PyTorch.
-
Debug and troubleshoot your code.
Create a project proposal
Show steps
Demonstrate your understanding of deep learning concepts and your ability to apply them to solve real-world problems.
Show steps
-
Identify a problem that you want to solve using deep learning.
-
Research existing deep learning solutions to similar problems.
-
Propose a deep learning model and explain how it will solve the problem.
-
Outline the steps involved in implementing and evaluating your model.
Participate in a deep learning competition
Show steps
Challenge yourself and showcase your skills in a competitive environment.
Show steps
-
Find a deep learning competition that aligns with your interests.
-
Form a team or work individually.
-
Develop and train a deep learning model.
-
Submit your model to the competition.
Review linear algebra
Show steps
Review the mathematical concepts that are fundamental to deep learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the concepts of vectors, matrices, and linear transformations.
- Practice solving linear equations and systems of equations.
- Review the concepts of eigenvalues and eigenvectors.
Read 'Deep Learning' by Goodfellow et al.
Show steps
Gain a comprehensive understanding of the foundations and applications of deep learning.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly.
- Take notes and highlight key concepts.
- Complete the exercises and assignments provided in the book.
Follow online tutorials on deep learning
Show steps
Supplement your classroom learning with additional resources and practical examples.
Show steps
Show steps
Show steps
- Find reputable online tutorials on deep learning.
- Follow the tutorials and complete the exercises.
- Research additional resources to expand your understanding.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Exposing yourself to content not directly related to the course will expand your worldview on the subject of Deep Learning and help you approach the framework of Deep Learning from a broader, more comprehensive vantage point
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Look over the table of contents
- Read the first three chapters
- Do the practice problems at the end of each chapter
Connect with deep learning experts
Show steps
Seek guidance and advice from experienced practitioners in the field.
Show steps
Show steps
Show steps
- Identify potential mentors through online forums, conferences, or research papers.
- Reach out to mentors and express your interest in their work.
- Meet with mentors regularly to discuss your progress and seek advice.
Participate in peer review sessions
Show steps
Gain valuable feedback on your work and learn from the experiences of others.
Show steps
Show steps
Show steps
- Attend peer review sessions.
- Present your work to others.
- Provide constructive feedback to your peers.
Complete coding exercises
Show steps
Sharpen your programming skills and apply your knowledge of deep learning algorithms.
Show steps
Show steps
Show steps
- Solve coding exercises related to deep learning algorithms.
- Practice implementing deep learning models using TensorFlow or PyTorch.
- Debug and troubleshoot your code.
Create a project proposal
Show steps
Demonstrate your understanding of deep learning concepts and your ability to apply them to solve real-world problems.
Show steps
Show steps
Show steps
- Identify a problem that you want to solve using deep learning.
- Research existing deep learning solutions to similar problems.
- Propose a deep learning model and explain how it will solve the problem.
- Outline the steps involved in implementing and evaluating your model.
Participate in a deep learning competition
Show steps
Challenge yourself and showcase your skills in a competitive environment.
Show steps
Show steps
Show steps
- Find a deep learning competition that aligns with your interests.
- Form a team or work individually.
- Develop and train a deep learning model.
- Submit your model to the competition.
Career center
Learners who complete AI Capstone Project with Deep Learning will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in developing and deploying deep learning models. This course provides Deep Learning Engineers with the knowledge and skills they need to succeed in this field. By learning how to apply deep learning techniques to real-world problems, Deep Learning Engineers can build innovative solutions to complex problems.
Researcher
Researcher
Researchers conduct original research to advance the field of deep learning. This course provides Researchers with the skills they need to develop and test deep learning models. By learning how to load and pre-process data, build and validate models, and present project reports, Researchers can become more proficient in their field.
Data Scientist
Data Scientist
Data Scientists collect and analyze data to help businesses make informed decisions. This course provides a strong foundation for Data Scientists by teaching them how to apply deep learning techniques to real-world problems. By understanding how to develop and test deep learning models, Data Scientists can gain valuable insights from data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course provides Machine Learning Engineers with the skills they need to develop and implement deep learning models. By learning how to load and pre-process data, build and validate models, and present project reports, Machine Learning Engineers can become more proficient in their field.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course provides Data Analysts with the skills they need to apply deep learning techniques to real-world problems. By understanding how to develop and test deep learning models, Data Analysts can gain valuable insights from data.
Educator
Educator
Educators teach students about deep learning. This course provides Educators with the skills they need to develop and teach deep learning courses. By learning how to load and pre-process data, build and validate models, and present project reports, Educators can become more effective in their teaching.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides Software Engineers with the skills they need to develop and implement deep learning models. By learning how to load and pre-process data, build and validate models, and present project reports, Software Engineers can become more proficient in their field.
Business Analyst
Business Analyst
Business Analysts help businesses improve their performance by identifying and solving problems. This course provides Business Analysts with the skills they need to apply deep learning techniques to real-world problems. By understanding how to develop and test deep learning models, Business Analysts can gain valuable insights from data and make better recommendations to businesses.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. This course provides Technical Writers with the skills they need to understand deep learning and how to write documentation for deep learning products. By learning how to develop and test deep learning models, Technical Writers can create more accurate and informative documentation.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products or services. This course provides Product Managers with the skills they need to understand the potential of deep learning and how it can be used to create innovative products. By learning how to develop and test deep learning models, Product Managers can make better decisions about how to develop and market their products.
Sales Engineer
Sales Engineer
Sales Engineers sell software and other technical products to businesses. This course provides Sales Engineers with the skills they need to understand the potential of deep learning and how it can be used to solve business problems. By learning how to develop and test deep learning models, Sales Engineers can make better recommendations to businesses about how to use deep learning to improve their operations.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns to promote products and services. This course provides Marketing Managers with the skills they need to understand the potential of deep learning and how it can be used to create more effective marketing campaigns. By learning how to develop and test deep learning models, Marketing Managers can make better decisions about how to target their audience and promote their products and services.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course provides Project Managers with the skills they need to manage deep learning projects. By understanding how to develop and test deep learning models, Project Managers can make better decisions about how to plan and execute their projects.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics. This course provides Consultants with the skills they need to advise businesses on the use of deep learning. By understanding how to develop and test deep learning models, Consultants can make better recommendations to businesses about how to use deep learning to improve their operations.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice to businesses and individuals. This course provides Financial Analysts with the skills they need to understand the potential of deep learning and how it can be used to make better financial decisions. By learning how to develop and test deep learning models, Financial Analysts can make better predictions about the future and make better recommendations to their clients.
For more career information including salaries, visit:
OpenCourser.com/course/bmrveo/ai
Reading list
We've selected eight 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
AI Capstone Project with Deep Learning .
Save
Provides a comprehensive overview of deep learning, covering the latest techniques and architectures. It valuable resource for anyone interested in learning more about deep learning.
Provides a comprehensive overview of deep learning, covering the latest techniques and architectures. It valuable resource for anyone interested in learning more about deep learning.
Provides a comprehensive overview of deep learning for natural language processing, covering the latest techniques and architectures. It valuable resource for anyone interested in learning more about deep learning for NLP.
Provides a comprehensive overview of deep learning for audio applications, covering the latest techniques and architectures. It valuable resource for anyone interested in learning more about deep learning for audio applications.
Provides a comprehensive overview of deep learning for finance, covering the latest techniques and architectures. It valuable resource for anyone interested in learning more about deep learning for finance.
Provides a comprehensive overview of deep learning for medical image analysis, covering the latest techniques and architectures. It valuable resource for anyone interested in learning more about deep learning for medical image analysis.
Provides a practical introduction to machine learning, using Python and popular libraries such as Scikit-Learn, Keras, and TensorFlow. It great resource for anyone interested in getting started with machine learning.
Provides a visual introduction to deep learning, using clear and concise explanations. It great resource for anyone who wants to understand the concepts behind deep learning without getting bogged down in the math.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bmrveo/ai
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Level
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Coursera
Institution
IBM
Instructors
Alex Aklson
Joseph Santarcangelo
Language
English
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Good to know
Builds a strong foundation for beginners exploring deep learning and computer vision
Examines deep learning model development, an essential skill for data scientists and machine learning engineers
Develops critical thinking and problem-solving skills
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