Programming Project: Image Recognition

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Related lectures (26)

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Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.

Quantum Decision-Making: Neural Network Privacy

Covers the quantum decision-making model and its implications for neural network privacy and image recognition.

Fine-Grained Visual Categorization: Challenges and Solutions

Explores challenges and solutions in fine-grained visual categorization, focusing on computer vision and machine learning.

NFNets: Removing BatchNorm for High-Performance Image Recognition

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Image Recognition: Datasets and Algorithms

Explores a 2019 paper on image recognition, dataset challenges, biases, and the impact of large-scale datasets on deep learning models.

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.

Segmentation: Techniques and Applications

Explores segmentation techniques, including CNNs and U-Net models, for image recognition and analysis, emphasizing time-saving automated methods.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Thermal Comfort Monitoring: Non-Intrusive Solutions

Presents a framework for non-intrusive thermal comfort monitoring using computer vision and machine learning techniques.

Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.