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Josh Bernhard ,
Mike Yi,
Judit Lantos,
David Drummond,
Andrew Paster,
Juno Lee,
and
Luis Serrano
Register for this course and see more details by visiting:
OpenCourser.com/course/od1mqb/capstone
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What's inside
Syllabus
Luis will give you an overview of logistic regression, gradient descent, and the building blocks of neural networks.
A deeper dive into backpropagation and the training process of neural networks, including techniques to improve the training.
Alexis explains the theory behind Convolutional Neural Networks and how they help us dramatically improve performance in image classification.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Benefits learners with an interest in machine learning
Features recognized instructors in the field of machine learning
Builds foundational skills in logistic regression and neural networks
Taught by Luis Serrano, Juno Lee, and Alexis, who are experts in their respective fields
Provides hands-on labs and interactive materials for practical learning
May require learners to have prior knowledge or experience in machine learning concepts
Save this course
Save [Capstone Content] Convolutional Neural Networks to your list so you can find it easily later:
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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 [Capstone Content] Convolutional Neural Networks with these
activities:
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Supplement course content with a foundational book on deep learning.
View
Deep Learning
on Amazon
Show steps
-
Acquire a copy of 'Deep Learning'.
-
Allocate time for reading and note-taking.
-
Engage with the material, highlighting and annotating key concepts.
Create a comprehensive collection of notes, assignments, and quizzes for future reference
Show steps
Improve knowledge retention and ease of revision by organizing your course materials.
Show steps
-
Gather all notes, assignments, quizzes, and other relevant materials into a single location.
-
Organize the materials into a logical structure, such as by topic or week.
-
Consider summarizing key concepts and highlighting important points.
Solve practice problems on logistic regression and backpropagation
Show steps
Strengthen your understanding of fundamental concepts through practice.
Browse courses on
Logistic Regression
Show steps
-
Identify online resources or textbooks with practice problems.
-
Attempt to solve the problems on your own, applying the concepts learned in the course.
-
Review your solutions and identify areas for improvement.
-
Seek clarification from the instructor or online forums if needed.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow a tutorial to understand Convolutional Neural Networks
Show steps
Supplement course content by exploring a tutorial on Convolutional Neural Networks.
Browse courses on
Convolutional Neural Networks
Show steps
-
Find a reputable resource for a Convolutional Neural Networks tutorial.
-
Follow the tutorial steps carefully, taking notes and asking questions in the discussion forum.
-
Apply what you learn to a project or exercise.
Practice coding and deploying Convolutional Neural Networks on a dataset
Show steps
Reinforce your practical skills in implementing and deploying neural networks.
Browse courses on
Convolutional Neural Networks
Show steps
-
Choose a relevant dataset for your project.
-
Implement a Convolutional Neural Network model for your dataset using a programming language and framework of your choice.
-
Train and evaluate your model on the chosen dataset.
-
Deploy your trained model to a cloud platform or create a user interface for it.
Form a weekly study group with classmates to discuss concepts and work on exercises
Show steps
Enhance your understanding and retention through collaborative learning.
Show steps
-
Identify a group of classmates with similar learning goals and schedules.
-
Set a regular time and place for your study sessions.
-
Take turns leading discussions, asking questions, and explaining concepts.
Create a blog post explaining gradient descent with examples
Show steps
Deepen your understanding of gradient descent by explaining it to others.
Browse courses on
Gradient Descent
Show steps
-
Review the course materials and outside resources on gradient descent.
-
Think of clear and relatable examples to illustrate the concept.
-
Write your blog post, ensuring clarity, accuracy, and engaging content.
-
Consider creating visual aids, such as diagrams or code snippets.
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Supplement course content with a foundational book on deep learning.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of 'Deep Learning'.
- Allocate time for reading and note-taking.
- Engage with the material, highlighting and annotating key concepts.
Create a comprehensive collection of notes, assignments, and quizzes for future reference
Show steps
Improve knowledge retention and ease of revision by organizing your course materials.
Show steps
Show steps
Show steps
- Gather all notes, assignments, quizzes, and other relevant materials into a single location.
- Organize the materials into a logical structure, such as by topic or week.
- Consider summarizing key concepts and highlighting important points.
Solve practice problems on logistic regression and backpropagation
Show steps
Strengthen your understanding of fundamental concepts through practice.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Identify online resources or textbooks with practice problems.
- Attempt to solve the problems on your own, applying the concepts learned in the course.
- Review your solutions and identify areas for improvement.
- Seek clarification from the instructor or online forums if needed.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow a tutorial to understand Convolutional Neural Networks
Show steps
Supplement course content by exploring a tutorial on Convolutional Neural Networks.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Find a reputable resource for a Convolutional Neural Networks tutorial.
- Follow the tutorial steps carefully, taking notes and asking questions in the discussion forum.
- Apply what you learn to a project or exercise.
Practice coding and deploying Convolutional Neural Networks on a dataset
Show steps
Reinforce your practical skills in implementing and deploying neural networks.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Choose a relevant dataset for your project.
- Implement a Convolutional Neural Network model for your dataset using a programming language and framework of your choice.
- Train and evaluate your model on the chosen dataset.
- Deploy your trained model to a cloud platform or create a user interface for it.
Form a weekly study group with classmates to discuss concepts and work on exercises
Show steps
Enhance your understanding and retention through collaborative learning.
Show steps
Show steps
Show steps
- Identify a group of classmates with similar learning goals and schedules.
- Set a regular time and place for your study sessions.
- Take turns leading discussions, asking questions, and explaining concepts.
Create a blog post explaining gradient descent with examples
Show steps
Deepen your understanding of gradient descent by explaining it to others.
Browse courses on
Gradient Descent
Show steps
Show steps
Show steps
- Review the course materials and outside resources on gradient descent.
- Think of clear and relatable examples to illustrate the concept.
- Write your blog post, ensuring clarity, accuracy, and engaging content.
- Consider creating visual aids, such as diagrams or code snippets.
Career center
Learners who complete [Capstone Content] Convolutional Neural Networks will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, test, and maintain machine learning models. They create models that can analyze large amounts of data and make predictions or automate tasks. [Capstone Content] Convolutional Neural Networks can help you to build a foundation towards this similar role and can help to prepare you for further study in computer science and advanced mathematics.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design, build, and implement computer vision systems that emulate the human visual system. Currently, computer vision is most frequently applied in image recognition and classification. After [Capstone Content] Convolutional Neural Networks, you will be adept at using NNs for image classification. This course will help you build a foundation towards this similar role and can help to prepare you for further study in computer science and advanced mathematics.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use statistical methods to develop models that can be used to make predictions or draw conclusions. [Capstone Content] Convolutional Neural Networks can help you to build a foundation towards this similar role and can help to prepare you for further study in statistics and computer science.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They use a variety of statistical and data mining techniques to identify trends and patterns in data. [Capstone Content] Convolutional Neural Networks can help you to build a foundation towards this similar role and can help to prepare you for further study in statistics and computer science.
Data Architect
Data Architect
Data Architects design and build data systems that meet the needs of an organization. They work with business stakeholders to understand their data needs and then design and implement data systems that can meet those needs. [Capstone Content] Convolutional Neural Networks can help you to build a foundation towards this similar role and can help to prepare you for further study in computer science and advanced mathematics.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. [Capstone Content] Convolutional Neural Networks can help you to build a foundation towards this role by giving you the quantitative and analytical skills you need to succeed.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in a variety of industries. They work on projects that involve logistics, supply chain management, and resource allocation. [Capstone Content] Convolutional Neural Networks can help you to develop the skills in math, statistics, and programming that this career role requires.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions about future financial performance. They are employed by investment banks, hedge funds, and other financial institutions. [Capstone Content] Convolutional Neural Networks can help you to build a foundation towards this role by giving you the quantitative and analytical skills you need to succeed.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. They work on a wide range of projects, from small personal apps to large enterprise systems. [Capstone Content] Convolutional Neural Networks can help you to build the fundamental computer science skills and the specific knowledge in neural networks that this career requires.
Data Scientist
Data Scientist
Data Scientists use their knowledge and skills in mathematics, computer science, and statistics to collect, analyze, and interpret large amounts of complex data. A solid foundation in statistics and computer science is essential to this role, and [Capstone Content] Convolutional Neural Networks can help you to build this foundation and may also be included as part of your required mathematics or computer science coursework.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data about markets and customers. They use this data to help businesses make informed decisions about product development, marketing, and pricing. [Capstone Content] Convolutional Neural Networks may be useful for developing your quantitative and analytical skills, both of which you will need in order to be successful.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make recommendations about investments. They analyze financial statements, market trends, and other data to identify undervalued or overvalued stocks. [Capstone Content] Convolutional Neural Networks may be useful for developing your quantitative and analytical skills, both of which you will need in order to be successful.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify opportunities for improvement and develop solutions to problems. They use a variety of analytical techniques to identify and solve problems, and they often work with data. [Capstone Content] Convolutional Neural Networks may be useful for developing your quantitative and analytical skills, both of which you will need in order to be successful.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They ensure that databases are running smoothly and that data is secure. [Capstone Content] Convolutional Neural Networks will not likely be directly applicable to your work as a Database Administrator, but the course will help you develop a good foundation in computer science and database systems, both of which you will need to succeed.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze data in the field of medicine. They work on projects that involve clinical trials, disease surveillance, and public health policy. [Capstone Content] Convolutional Neural Networks may be useful for developing some of the quantitative skills, though likely not the medical or biological expertise you will need to be successful.
For more career information including salaries, visit:
OpenCourser.com/course/od1mqb/capstone
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
[Capstone Content] Convolutional Neural Networks.
Save
This comprehensive textbook provides a deep dive into the theory and practice of deep learning, covering foundational concepts, architectures, and applications.
A hands-on introduction to deep learning using Python and the Keras library, covering essential concepts and techniques.
Save
A comprehensive introduction to computer vision, providing foundational knowledge and algorithms for image processing, object recognition, and scene understanding.
A practical guide to using deep learning for computer vision tasks, covering image classification, object detection, and image segmentation.
Save
A gentle introduction to deep learning, with a focus on the mathematical foundations and practical applications.
Save
A hands-on guide to deep learning using R and the Keras library, covering essential concepts and techniques.
A practical guide to building and training convolutional neural networks for computer vision tasks using Python and the Keras library.
A hands-on guide to deep learning using JavaScript and the TensorFlow.js library, covering essential concepts and techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/od1mqb/capstone
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Similar courses
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Time
900 hours
Level
Advanced
Via
Udacity
Instructors
Josh Bernhard
Mike Yi
Judit Lantos
David Drummond
Andrew Paster
Juno Lee
Luis Serrano
Language
English
Good to know
Benefits learners with an interest in machine learning
Features recognized instructors in the field of machine learning
Builds foundational skills in logistic regression and neural networks
Taught by Luis Serrano, Juno Lee, and Alexis, who are experts in their respective fields
Provides hands-on labs and interactive materials for practical learning
May require learners to have prior knowledge or experience in machine learning concepts
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