Deep Learning with PyTorch
Neural architectures and PyTorch development, exploring advanced concepts like CNNs, data classification, and dimension optimization methods.
Neural architectures and PyTorch development, exploring advanced concepts like CNNs, data classification, and dimension optimization methods.
This comprehensive course, the second part of a two-part series, focuses on building and training deep neural networks using PyTorch. Starting with multiclass classification, students learn to construct feed-forward neural networks and master state-of-the-art training methods. The curriculum covers essential topics including dropout, initialization, optimizers, and batch normalization. Advanced concepts like Convolutional Neural Networks, GPU training, and Transfer Learning are explored in detail. The course concludes with dimensionality reduction techniques and autoencoder applications, culminating in a practical final project. Students gain hands-on experience with PyTorch while building complex deep learning pipelines.
3.7
(13 ratings)
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What you'll learn
Apply advanced Deep Neural Network concepts in practical scenarios
Construct and optimize complex neural architectures using PyTorch
Implement state-of-the-art training methods and optimization techniques
Design and train Convolutional Neural Networks for computer vision tasks
Utilize GPU acceleration for efficient model training
Master dimensionality reduction and autoencoder applications
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 7 modules in this course
This advanced Deep Learning course provides comprehensive coverage of neural network architectures and training methodologies using PyTorch. Students learn to implement multiclass classification, construct and optimize feed-forward neural networks, and master advanced techniques like dropout and batch normalization. The course emphasizes practical applications through hands-on projects, covering crucial topics such as Convolutional Neural Networks, GPU acceleration, and transfer learning. Special attention is given to dimensionality reduction techniques and autoencoder applications, ensuring students gain both theoretical understanding and practical implementation skills.
Classification
Module 1
Neural Networks
Module 2
Deep Networks
Module 3
Computer Vision Networks
Module 4
Computer Vision Networks
Module 5
Dimensionality reduction and autoencoders
Module 6
Independent Project
Module 7
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.
Testimonials
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3.7 course rating
13 ratings
Frequently asked questions
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