Deep Neural Networks with PyTorch
Master deep learning with PyTorch: Build and train neural networks from basic to advanced architectures including CNN and transfer learning.
Master deep learning with PyTorch: Build and train neural networks from basic to advanced architectures including CNN and transfer learning.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full IBM AI Engineering Professional Certificate program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.4
(1,683 ratings)
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Instructors:
English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Implement deep neural networks using PyTorch
Master tensor operations and automatic differentiation
Build and train various neural network architectures
Optimize models using advanced techniques and GPU acceleration
Skills you'll gain
This course includes:
6.5 Hours PreRecorded video
1 quiz, 42 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 6 modules in this course
This comprehensive course covers deep learning implementation using PyTorch. Starting with fundamental concepts like tensors and automatic differentiation, students progress through various neural network architectures and training techniques. The curriculum includes linear regression, logistic regression, feedforward neural networks, and convolutional neural networks. Advanced topics cover activation functions, normalization, dropout layers, and optimization techniques. The course emphasizes hands-on learning with extensive programming assignments and practical applications.
Tensor and Datasets
Module 1 · 4 Hours to complete
Linear Regression
Module 2 · 2 Hours to complete
Linear Regression PyTorch Way
Module 3 · 2 Hours to complete
Multiple Input Output Linear Regression
Module 4 · 1 Hours to complete
Logistic Regression for Classification
Module 5 · 1 Hours to complete
Practice Project and Final Project
Module 6 · 3 Hours to complete
Fee Structure
Instructor
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
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4.4 course rating
1,683 ratings
Frequently asked questions
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