Convolutional Neural Networks with PyTorch
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Course
Convolutional Neural Networks with PyTorch
Gain hands-on experience in computer vision using PyTorch.
Explore convolutional neural networks (CNNs) and apply them to real-world image analysis tasks.
Flexible Schedule
Beginner Level
Mentor Support
Estimated Time
Convolutional neural networks (CNNs) are the backbone of modern computer vision, enabling breakthroughs in image recognition, object detection, and more. This Convolutional Neural Networks with PyTorch course introduces you to the fundamentals of CNNs and shows you how to implement them step by step using PyTorch.
In this course, youll start by exploring convolutional layers, pooling, and activation functions, then progress to advanced architectures that power real-world AI systems. Through hands-on coding exercises and projects, youll learn to preprocess data, train effective models, and evaluate performance with confidence.
By combining theory with practice, this course equips you with the skills to design and deploy CNNs for industry-relevant applications. If youre looking to deepen your deep learning expertise and unlock career opportunities in AI, this course is your next step.
This course comprises one purposely designed module that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules. It is anticipated that you will complete the course in 4 hours. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
You will be able to:
- Apply AI techniques to solve supervised classification problems.
- Build, train, and evaluate deep learning models using the PyTorch framework.
- Write efficient Python code to preprocess data and implement machine learning workflows.
- Developers or data scientists looking to build image classifiers
- Anyone interested in convolutional neural networks (CNNs) and transfer learning.
Good understanding of PyTorch tensors and datasets, linear regression and classification, and neural networks principles.
Course Outline
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Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
This course introduces you to the core concepts of CNN in PyTorch, including convolution, max pooling, and convolutional network architectures. Youll learn how to build CNN with PyTorch, train models on GPUs, and apply transfer learning using pre-trained models like ResNet, VGG, and AlexNet.
Anyone pursuing computer vision, image analysis, or deep learning projects will benefit. Its best suited for learners who already have some PyTorch fundamentals and want to advance into CNN in PyTorch.
Yes, since this is part of the PyTorch Learning Path, you should be comfortable with PyTorch Tensors, datasets, linear regression, classification, and neural network principles before starting.
The course covers convolution operations, max pooling for downsampling, and the structure of convolutional, pooling, and fully connected layers. Youll also practice training CNN in PyTorch with real datasets.
Yes, youll get practical experience building and training CNN in PyTorch, applying GPU acceleration, and solving image classification problems with real-world datasets.
Training CNN with PyTorch on GPUs significantly reduces training time, improves model performance, and enables larger datasets and more complex models to be handled efficiently.
Yes, youll learn how to build CNN with PyTorch by leveraging pre-trained models for transfer learning, enabling you to quickly adapt models to new image analysis tasks.
After completing the course, youll be able to use CNN in PyTorch for tasks like object recognition, image classification, and applying transfer learning for domain-specific image datasets.
Youll gain expertise in convolution, pooling, network architecture design, GPU acceleration, and transfer learning. By the end, youll be able to build CNN with PyTorch for real-world image analysis applications.
Yes, upon successful completion, youll receive an IBM Certificate that validates your skills in convolutional neural networks with PyTorch.
Yes, it is part of the PyTorch Learning Path, which also includes courses on tensors, datasets, linear regression, classification, and building neural networks before progressing into CNN in PyTorch.
Type of certificate
IBM Certificate
About this course
01 Module
03 Skills
Includes
Discussion space
06 Hands-on labs
05 Quizzes
Exercises to explore
What's Convolution
Activation Function and Max Pooling
Multiple Input and Output Channels
Convolutional Neural Network
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