Deep Learning Fundamentals and Neural Network Architectures
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This course provides an in-depth introduction to deep neural networks, covering their historical roots, key concepts, and modern applications. Students will explore the fundamentals of neural networks, including linear networks and components, and delve into practical implementations using PyTorch on GPU clusters. The course will discuss the evolution of neural networks, from their early beginnings to recent breakthroughs, and highlight the importance of understanding individual components to achieve optimal performance and generalization ability in deep learning. Through lectures and lab work, students will gain hands-on experience with neural networks, including convolutional, recurrent, and generative networks, and develop a solid foundation in this transformative field.
Date de création :
30 novembre 2020
Intervenants :
Jeremy Fix
Société :
CentraleSupélec
Lien vers la chaîne du média :
Mention SDI - Metz -