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Related lectures (32)

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Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Modern Convolutional Networks and Image Recognition

Explores the evolution of deep convolutional networks and their impact on image recognition accuracy.

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Support Vector Machines: Theory and Applications

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

Self-Supervised Learning: State of the Art

Explores self-supervised learning, transfer learning, SSL prediction tasks, feature learning, image rotations, contrastive learning, and vision learners.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Bullet Arm: Robotic Manipulation Benchmark

Introduces BulletArm, an open-source robotic manipulation benchmark and learning framework, covering design goals, benchmark tasks, and learning algorithms.

Feedforward Neural Networks: Activation Functions and Backpropagation

Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.

Statistical Learning Theory: Conclusions on Deep Learning

Covers the conclusions on deep learning and an introduction to statistical learning theory.

Transfer Learning with CNNs

Explores transfer learning with CNNs, fine-tuning, and network depth impact.