Deep Learning with PyTorch
Master deep learning techniques using PyTorch. From softmax regression to CNNs, gain hands-on experience in neural network development.
Master deep learning techniques using PyTorch. From softmax regression to CNNs, gain hands-on experience in neural network development.
This comprehensive course covers advanced machine learning and deep learning concepts using PyTorch. Students will progress from fundamental techniques like softmax regression to complex models such as convolutional neural networks. The curriculum includes shallow and deep neural networks, activation functions, dropout, weight initialization, batch normalization, and GPU acceleration. Through hands-on labs and a final project, learners will gain practical experience in implementing these techniques for various applications, including image classification. Suitable for intermediate learners with basic Python and mathematical knowledge, this course prepares students for real-world AI engineering challenges.
Instructors:
English
Deutsch, हिन्दी, پښتو, 24 more
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What you'll learn
Understand and implement softmax regression for multi-class classification
Develop and train shallow neural networks with various architectures
Master key concepts of deep neural networks, including dropout and batch normalization
Implement and optimize convolutional neural networks (CNNs) for image processing tasks
Apply different activation functions and understand their impact on model performance
Utilize PyTorch's nn.Module and nn.Sequential for efficient model construction
Skills you'll gain
This course includes:
2.26 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 6 modules in this course
This course provides a comprehensive exploration of deep learning techniques using PyTorch. Students will progress from fundamental concepts like softmax regression to advanced topics such as convolutional neural networks. The curriculum covers the development and training of shallow and deep neural networks, including techniques like dropout, weight initialization, and batch normalization. Learners will gain hands-on experience with various neural network architectures, activation functions, and optimization methods. The course also introduces GPU acceleration for deep learning tasks. Through practical labs and a final project on image classification, students will develop the skills necessary to implement and optimize deep learning models for real-world applications.
Logistic Regression Cross Entropy Loss
Module 1 · 1 Hours to complete
Softmax Regression
Module 2 · 1 Hours to complete
Shallow Neural Networks
Module 3 · 3 Hours to complete
Deep Networks
Module 4 · 4 Hours to complete
Convolutional Neural Networks
Module 5 · 5 Hours to complete
Final Project
Module 6 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Ph.D., Data Scientist at IBM
Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
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Frequently asked questions
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