deep learning study (introduction) #1

Deep learning lecture
(tensor flow based..)

part 1.

1. logistic classification

2. stochastic optimization

3. general data practices to train models( data & parameter tuning)


part 2. (we're going to go deeper)

1. Deep networks

2. Regularization (to train even bigger models)


part 3. ( will be a deep dive into image and convolutional models)

1. convolutional networks


part 4. (all about text and sequence in general)

1. embeddings

2. recurrent models