Large Scale Data Analysis Using Deep Learning

coursetab

c_lecno	Title	files
00	Syllabus	L0-Syllabus.pdf
01	Course Introduction	L1-Course Introduction.pdf
02	Introduction to Deep Learning	L2-Introduction to Deep Learning.pdf
03	Linear Algebra	L3-Linear Algebra.pdf
04	Probability and Information Theory	L4-Probability and Information Theory.pdf
05	Numerical Computation	L5-Numerical Computation.pdf
06	Machine Learning Basics 1	L6-Machine Learning Basics 1.pdf
07	Machine Learning Basics 2	L7-Machine Learning Basics 2.pdf
08	Deep Feedforward Networks	L8-Deep Feedforward Networks.pdf
09	Deep Feedforward Networks 2	L9-Deep Feedforward Networks 2.pdf
10	Introduction to Tensorflow	L10-Introduction to Tensorflow.pdf
11	Convolutional Neural Network	L11-Convolutional Neural Network.pdf
12	Recurrent Neural Network	L12-Recurrent Neural Network.pdf
13	Regularization for Deep Learning	L13-Regularization for Deep Learning.pdf
14	Regularization for Deep Learning 2	L14-Regularization for Deep Learning 2.pdf
15	Optimization for Training Deep Models	L15-Optimization for Training Deep Models.pdf
16	Optimization for Training Deep Models 2	L16-Optimization for Training Deep Models 2.pdf
17	Practical Methodology	L17-Practical Methodology.pdf
18	Applications	L18-Applications.pdf
19	Autoencoder	L19-Autoencoder.pdf