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Related lectures (28)
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Modern Convolutional Networks and Image Recognition
Explores the evolution of deep convolutional networks and their impact on image recognition accuracy.
Deep Learning: Convolutional Neural Networks and Training Techniques
Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.
Deep Learning: No Free Lunch Theorem and Inductive Bias
Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.
Understanding Visual Cortex with Deep Learning Models
Explores using deep learning models to understand the visual cortex and sensory portraits.
Transformer Architecture: The X Gomega
Delves into the Transformer architecture, self-attention, and training strategies for machine translation and image recognition.
Introduction to Machine Learning
Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.
Vision-based robotic perception
Explores challenges and opportunities in vision-based robotic perception, covering topics like SLAM, place recognition, event cameras, and collaborative visual intelligence.
Graph Search: Neural Networks and Deep Learning
Delves into graph search, neural networks, and deep learning, covering topics like convolutional neural networks and artificial neural networks.
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