Neoantigen Prediction: Ground Truths and AI

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Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Explores segmentation techniques in image analysis, including thresholding, clustering, region growing, and machine learning.

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Introduces K-Means Clustering, a simple yet effective algorithm for grouping data points into clusters.

Delves into challenges of supervised learning in citizen science, focusing on plant species recognition and label aggregation.

Explores the historical development of computer vision and inspires innovative project ideas.

Covers the fundamentals of computer vision, illusions, challenges, applications, and history.

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