Build a Neural Network with PyTorch
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Course
Build a Neural Network with PyTorch
Learn to design, train, and optimize neural networks using PyTorch. Develop hands-on expertise for a career in deep learning and AI.
Self-Paced
Mentored
Beginner
Duration
Neural networks are at the core of modern artificial intelligence (AI), powering applications from image recognition to natural language processing. Professionals skilled in building and training neural networks are highly sought after in the AI and machine learning industry.
In this course, you will explore the fundamentals of neural networks and gain practical experience using PyTorch, one of the most popular deep learning frameworks. You will learn to build, train, and optimize neural networks through interactive lectures, coding exercises, and hands-on projects.
As you progress through the course, youll apply your knowledge to real-world machine learning challenges, gaining the skills and confidence to design, train, and deploy neural networks using PyTorch.
For individuals aiming to develop career-ready expertise in deep learning, this course provides a solid foundation in neural network development and practical experience with PyTorch.
This course comprises two purposely designed modules 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 7 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 concepts to solve real-world problems.
- Build and train deep learning models using PyTorch.
- Write efficient Python code for AI and machine learning workflows.
Anyone eager to build a strong foundation in deep learning and neural networks using PyTorch.
Must have a good understanding of PyTorch Tensors and DataSets, linear regression, and classification.
Course Outline
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
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
In this course, youll learn how to build neural networks with PyTorch, including key concepts like back propagation, activation functions, dropout, batch normalization, and other optimization methods. Youll also gain practical skills through coding exercises and projects to train and deploy deep neural networks.
Basic knowledge of Python is required. For PyTorch, this course is part of the PyTorch Learning Path, so either completing the prerequisite courses or having a solid understanding of PyTorch tensors, datasets, linear regression, and classification is recommended.
Yes. Youll apply your learning through interactive lectures, hands-on coding exercises, and projects that allow you to build and train neural networks with PyTorch in practical, real-world scenarios.
Yes. The course covers essential neural network optimization techniques, including dropout, initialization strategies, batch normalization, and back propagation, giving you a strong foundation for building deep neural networks with PyTorch.
Absolutely. The course is designed to give you a step-by-step introduction to neural networks with PyTorch, starting from fundamental concepts to building and deploying deep neural networks, making it ideal for beginners who want to learn PyTorch fundamentals and deep learning applications.
By completing this course, you will gain practical experience and confidence in PyTorch. Youll learn how to structure, train, and optimize neural networks, giving you a strong foundation to continue with deep learning with PyTorch or pursue advanced PyTorch training courses.
Yes. Upon completion, you will receive an IBM Certificate, which can be used to showcase your knowledge of neural networks with PyTorch and foundational deep learning skills to employers or for continuing education in AI and machine learning.
This course is ideal for students and professionals aiming for roles such as deep learning engineer, neural network developer, or AI engineer. It provides foundational skills necessary to pursue more advanced PyTorch projects and machine learning careers.
Yes. This course is part of a comprehensive PyTorch learning path, which includes courses on PyTorch tensors, datasets, linear regression, and classification, ensuring a structured progression from fundamentals to advanced neural network applications.
Type of certificate
IBM Certificate
About this course
02 Modules
03 Skills
Includes
Discussion space
16 Hands-on labs
12 Quizzes
Create
Activation Function
Neural Network Momentum
Batch Normalization
Exercises to explore
Neural Networks
Multi-Class Neural Networks with MNIST
Deep Neural Networks
Dropout Classification & Regression
Initialization Xavier
Momentum with Different Polynomials
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