Machine Learning for Medicine: Insights and Interpretability

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Related lectures (31)

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Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Kohonen Maps: Summary

Summarizes Kohonen maps, covering initialization, sampling, similarity-matching, examples, and applications in machine learning and data classification.

Machine Learning in Molecular Dynamics

Explores machine learning in molecular dynamics simulations, addressing the curse of dimensionality, neural network representation, and force-field estimation.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Neural Networks: Training and Optimization

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Understanding Deep Learning

Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.

Optical Machine Learning: Harnessing Multiple Scattering for Efficiency

Explores the use of optics in machine learning, focusing on large-scale random matrix multiplication through multiple scattering of light.

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.