Content

Part I Basics

1. Intro

   1. PreReq (appendix with links to tutorials/install instrcutions/etc);

   2. What is DL(and what it is not)? (Place of DL amount other ML algorithms?)

      1. main myths / ...

   3. Why now?

      1. showcase (GooglePhotos, Alexa, Medical chatBot)

   4. Our goals for this book

      1. structure

      2. goals per parts

2. Our first Neural Network

   1. What is neuron

   2. What is activation functions

   3. problem (3 nerons network)

   4. simple 1 layer perseptron

3. How to to train Neural Network:

   1. loss/error;

   2. Participation of Neuron in Error;

   3. Optimizer;

   4. SGD;

Part II Real Life Example

1. What is MNIst;

2. First try:

   1. introudce differnt activation function;

   2. improve speed:

      1. batching;

      2. LA;

Part II NN Architectures (?)

1. CNN

2. RNN

Part III DL frameworks

Part IV Architecturing Real Model

1. Hand digit recognition

2. Object detection

3. Machine translation

4. Chat bot creation

Part V Industrial projects

1. Creating Chat bot REAST API with AWS SageMaker/AWS Lambda and AWS API GateWay;

2. A/B testing new model with AWS SageMaker;

3. TODO

Efterwards (beyond the book)/what next?

TODO