Neural Network Training

Related lectures (30)

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Explores Matlab programming with scripts and functions, vectorization, and 2D graphics.

Covers logical arrays, 3D surface plotting, parametric curves, interpolation, and fitting in Matlab.

Introduces Matlab basics, error handling, and billiards project concepts.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Explores tissue-level wave patterns, cellular oscillators, genetic waves, and gene expression patterns in cellular differentiation.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

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

Covers the application of numerical methods to solve differential equations using MATLAB.

Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.