Sketching the Journey of Learning

Bali's notes & demos.

Page 3 of 4 for Sketching the Journey of Learning | Bali’s notes & demos.

Notes for Prof. Hung-Yi Lee's ML Lecture 11: Why Deep?

Having the same total number of parameters, the deep, i.e. thin and tail, neural networks, performs better than the fat abd short networks. The reason is that deep neural networks benefits from modularization, so they use the data and the parameters more efficiecntly.

Notes for Prof. Hung-Yi Lee's ML Lecture 10: CNN

CNN: Convolutional Neural Networks

Notes for Prof. Hung-Yi Lee's ML Lecture 09: Tips for Deep Learning

Steps Toward a Successful DL Training

Notes for Prof. Hung-Yi Lee's ML Lecture 7: Backpropagation

For applying gradient descent on neural networks, there may be millions or more parameters, and we use backpropagation to compute the gradients efficiently.

Notes for Prof. Hung-Yi Lee's ML Lecture 6: Brief Introduction to Deep Learning

History of and Remarks on Deep Learning

Notes for Prof. Hung-Yi Lee's ML Lecture 5, Classification: Logistic Regression.

Logistic Regression

Notes for Prof. Hung-Yi Lee's ML Lecture 4, Classification: Probabilistic Generative Model.

What are Classification Problems

Notes for Prof. Hung-Yi Lee's ML Lecture 3, gradient descent.

Vanilla Gradient Descent Formulation

Notes for Prof. Hung-Yi Lee's ML Lecture 2, Sources of error.

Sources of error Bias Variance

Notes for Prof. Hung-Yi Lee's ML Lecture 1, Regression

Notations