Physics of Biological Systems

Related lectures (30)

Page 3 of 3

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Explores Systems Biology, metabolic pathways, genome-scale models, and bioinformatic tools for predictive biochemistry in bioproduction.

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Explores light microscopy principles, fluorescent proteins, optical slicing, and modulation transfer functions in cellular imaging.

Explores the challenges of applying Newton's laws at the cellular scale and the importance of relevant coordinates and scales in cell biology.

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

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

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Explores two-layer neural networks and backpropagation for learning feature spaces and approximating continuous functions.