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Nathan Klarer
Gain the knowledge and skills to train convolutional neural networks. Our comprehensive CNN training course will help you understand the fundamentals of CNNs
Prerequisite details
Read more
Gain the knowledge and skills to train convolutional neural networks. Our comprehensive CNN training course will help you understand the fundamentals of CNNs
Prerequisite details
Read more
Gain the knowledge and skills to train convolutional neural networks. Our comprehensive CNN training course will help you understand the fundamentals of CNNs
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Deep learning fluency
- Neural network basics
- Intermediate Python
- Backpropagation
- Gradient descent
- Overfitting prevention
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/y3w06b/convolutional
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What's inside
Syllabus
In this lesson we will look at the main applications of CNNs, understand professional roles involved in the development of a CNN-based application, and learn about the history of CNNs.
Read more
Syllabus
In this lesson we will look at the main applications of CNNs, understand professional roles involved in the development of a CNN-based application, and learn about the history of CNNs.
Read more
In this lesson we will recap how to use a Multi-Layer Perceptron for image classification, understand the limitations of this approach, and learn how CNNs can overcome these limitations.
In this lesson we will study in depth the basic layers used in CNNs, build a CNN from scratch in PyTorch, use it to classify images, improve its performance, and export it for production.
In this lesson we will learn about key CNN architectures and their innovations, and apply multiple ways of adapting them to our use cases with transfer learning.
In this lesson we will design and train linear and CNN-based autoencoders for anomaly detection and for image denoising.
In this lesson we will study applications of CNNs beyond image classification. We will train and evaluate an object detection model as well as a semantic segmentation model on custom datasets.
In this project, you will apply the skills you have acquired in the Convolutional Neural Network (CNN) course to build a landmark classifier.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides grounding in neural networks and advanced computational frameworks for training and optimizing CNNs
Suitable for professionals and students in data science, machine learning, computer vision, and related fields who want to gain expertise in CNNs
Led by respected instructors with practical industry experience in CNN applications
Delivers hands-on, practical learning through exercises, projects, and industry use cases
Covers essential concepts of image classification, object detection, and semantic segmentation using CNNs
Includes a project where learners build a landmark classifier, applying their acquired skills in CNN training
Save this course
Save 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 Convolutional Neural Networks with these
activities:
CNN Resources Compilation
Show steps
Gather and organize useful resources related to CNNs, such as articles, tutorials, datasets, and code libraries.
Browse courses on
CNNs
Show steps
-
Search for relevant resources online and create a list of links.
-
Categorize the resources into different sections, such as tutorials, datasets, and code libraries.
-
Create a document or database to store and organize the resources.
-
Share your compilation with classmates or online communities.
Deep Learning for Coders with Fastai and PyTorch
Show steps
Expand your understanding of CNNs by reading a book that provides a practical approach to deep learning using popular frameworks like Fastai and PyTorch.
View
Deep Learning for Coders with Fastai and...
on Amazon
Show steps
-
Read chapters covering the fundamentals of CNNs.
-
Study code examples and experiment with the provided Jupyter notebooks.
-
Apply the concepts to real-world image classification tasks.
Solve Backpropagation Problems
Show steps
Deepen your understanding of backpropagation, a critical component of training CNNs, by solving exercises.
Browse courses on
Backpropagation
Show steps
-
Review the mathematical equations for backpropagation.
-
Set up a simple neural network architecture and implement backpropagation manually.
-
Test your implementation on a dataset and analyze the results.
-
Experiment with different learning rates and activation functions to optimize your model.
Four other activities
Expand to see all activities and additional details
Show all seven activities
PyTorch CNN Tutorials
Show steps
Enhance your practical skills by following step-by-step tutorials that guide you through building CNNs using the PyTorch library.
Browse courses on
CNNs
Show steps
-
Find online tutorials or courses that cover CNNs with PyTorch.
-
Follow the instructions to create a CNN model for a specific task.
-
Run the model and analyze the results to understand its performance.
-
Experiment with different CNN architectures and hyperparameters.
Mentor a Junior in CNNs
Show steps
Deepen your understanding and reinforce your skills by mentoring a junior learner who is starting their journey with CNNs.
Browse courses on
CNNs
Show steps
-
Identify a junior learner who is interested in learning about CNNs.
-
Meet regularly to provide guidance, answer questions, and share resources.
-
Review the learner's code and provide feedback on their progress.
-
Encourage the learner to ask questions and participate in discussions.
-
Celebrate the learner's successes and provide support during challenges.
Build a CNN Image Classifier from Scratch
Show steps
Solidify your knowledge of CNNs by implementing a custom CNN model from scratch to solve an image classification problem.
Browse courses on
CNNs
Show steps
-
Choose an image classification dataset and pre-process the data.
-
Design a CNN architecture with appropriate layers and activation functions.
-
Implement the forward and backward pass of your CNN using PyTorch or TensorFlow.
-
Train your model and evaluate its performance on a validation set.
-
Deploy your model for inference and use it to classify new images.
Kaggle Image Classification Competition
Show steps
Test your skills by participating in a Kaggle competition that challenges you to develop a CNN model for a large-scale image classification task.
Browse courses on
CNNs
Show steps
-
Explore the competition dataset and familiarize yourself with the task.
-
Develop a CNN model and tune its hyperparameters using cross-validation.
-
Submit your model and track its performance on the competition leaderboard.
-
Analyze the results and compare your approach with others.
CNN Resources Compilation
Show steps
Gather and organize useful resources related to CNNs, such as articles, tutorials, datasets, and code libraries.
Browse courses on
CNNs
Show steps
Show steps
Show steps
- Search for relevant resources online and create a list of links.
- Categorize the resources into different sections, such as tutorials, datasets, and code libraries.
- Create a document or database to store and organize the resources.
- Share your compilation with classmates or online communities.
Deep Learning for Coders with Fastai and PyTorch
Show steps
Expand your understanding of CNNs by reading a book that provides a practical approach to deep learning using popular frameworks like Fastai and PyTorch.
View
Deep Learning for Coders with Fastai and...
on Amazon
Show steps
Show steps
Show steps
- Read chapters covering the fundamentals of CNNs.
- Study code examples and experiment with the provided Jupyter notebooks.
- Apply the concepts to real-world image classification tasks.
Solve Backpropagation Problems
Show steps
Deepen your understanding of backpropagation, a critical component of training CNNs, by solving exercises.
Browse courses on
Backpropagation
Show steps
Show steps
Show steps
- Review the mathematical equations for backpropagation.
- Set up a simple neural network architecture and implement backpropagation manually.
- Test your implementation on a dataset and analyze the results.
- Experiment with different learning rates and activation functions to optimize your model.
Four other activities
Expand to see all activities and additional details
Show all seven activities
PyTorch CNN Tutorials
Show steps
Enhance your practical skills by following step-by-step tutorials that guide you through building CNNs using the PyTorch library.
Browse courses on
CNNs
Show steps
Show steps
Show steps
- Find online tutorials or courses that cover CNNs with PyTorch.
- Follow the instructions to create a CNN model for a specific task.
- Run the model and analyze the results to understand its performance.
- Experiment with different CNN architectures and hyperparameters.
Mentor a Junior in CNNs
Show steps
Deepen your understanding and reinforce your skills by mentoring a junior learner who is starting their journey with CNNs.
Browse courses on
CNNs
Show steps
Show steps
Show steps
- Identify a junior learner who is interested in learning about CNNs.
- Meet regularly to provide guidance, answer questions, and share resources.
- Review the learner's code and provide feedback on their progress.
- Encourage the learner to ask questions and participate in discussions.
- Celebrate the learner's successes and provide support during challenges.
Build a CNN Image Classifier from Scratch
Show steps
Solidify your knowledge of CNNs by implementing a custom CNN model from scratch to solve an image classification problem.
Browse courses on
CNNs
Show steps
Show steps
Show steps
- Choose an image classification dataset and pre-process the data.
- Design a CNN architecture with appropriate layers and activation functions.
- Implement the forward and backward pass of your CNN using PyTorch or TensorFlow.
- Train your model and evaluate its performance on a validation set.
- Deploy your model for inference and use it to classify new images.
Kaggle Image Classification Competition
Show steps
Test your skills by participating in a Kaggle competition that challenges you to develop a CNN model for a large-scale image classification task.
Browse courses on
CNNs
Show steps
Show steps
Show steps
- Explore the competition dataset and familiarize yourself with the task.
- Develop a CNN model and tune its hyperparameters using cross-validation.
- Submit your model and track its performance on the competition leaderboard.
- Analyze the results and compare your approach with others.
Career center
Learners who complete Convolutional Neural Networks will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design and develop deep learning models. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient deep learning models.
Computer Vision Scientist
Computer Vision Scientist
Computer Vision Scientists develop new computer vision algorithms and techniques. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient computer vision algorithms and techniques.
Medical Image Analyst
Medical Image Analyst
Medical Image Analysts analyze medical images to identify abnormalities and diseases. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient medical image analysis techniques.
Image Processing Engineer
Image Processing Engineer
Image Processing Engineers design and develop software that processes images. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient image processing software.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop software that enables computers to see and interpret images and videos. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for computer vision tasks. With this knowledge, you will be able to develop more accurate and efficient computer vision systems.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers develop new artificial intelligence algorithms and techniques. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient artificial intelligence algorithms and techniques.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient data science models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient machine learning models.
Bioinformatician
Bioinformatician
Bioinformaticians use computational tools to analyze biological data. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient bioinformatics tools.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient operations research models.
Software Engineer
Software Engineer
Software Engineers design and develop software applications. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient software applications.
Product Manager
Product Manager
Product Managers develop and manage products. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more innovative and successful products.
Data Analyst
Data Analyst
Data Analysts analyze data to identify trends and patterns. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient data analysis techniques.
UX Designer
UX Designer
UX Designers design and develop user interfaces. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more user-friendly and efficient user interfaces.
Robotics Engineer
Robotics Engineer
Robotics Engineers design and develop robots. This course can help you build a foundation in convolutional neural networks, which are a type of deep learning model that is particularly well-suited for image and video analysis. With this knowledge, you will be able to develop more accurate and efficient robots.
For more career information including salaries, visit:
OpenCourser.com/course/y3w06b/convolutional
Reading list
We've selected 11 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
Convolutional Neural Networks.
Save
Provides a comprehensive overview of deep learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about deep learning.
Save
Provides a comprehensive overview of computer vision, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about computer vision.
Provides a practical guide to machine learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about machine learning.
Provides a practical guide to deep learning with Python, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about deep learning with Python.
Provides a comprehensive overview of statistical learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about statistical learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about pattern recognition and machine learning.
Save
Provides a practical guide to deep learning with R. It valuable resource for anyone interested in learning more about deep learning with R.
Provides a practical guide to machine learning with R. It valuable resource for anyone interested in learning more about machine learning with R.
Provides a practical guide to deep learning for computer vision. It valuable resource for anyone interested in learning more about deep learning for computer vision.
Provides a comprehensive overview of deep reinforcement learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about deep reinforcement learning.
Save
Provides a comprehensive overview of generative adversarial networks, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about generative adversarial networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y3w06b/convolutional
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Effort
4 weeks
Via
Udacity
Instructor
Nathan Klarer
Language
English
Good to know
Provides grounding in neural networks and advanced computational frameworks for training and optimizing CNNs
Suitable for professionals and students in data science, machine learning, computer vision, and related fields who want to gain expertise in CNNs
Led by respected instructors with practical industry experience in CNN applications
Delivers hands-on, practical learning through exercises, projects, and industry use cases
Covers essential concepts of image classification, object detection, and semantic segmentation using CNNs
Includes a project where learners build a landmark classifier, applying their acquired skills in CNN training
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