1 Introduction

2 High Level Considerations for Deep Learning

3 Deep Learning Libraries and Resources

4 Standard Neural Network Models and Tasks

5 Key (Supervised Learning) Methods

6 Doing More with Less Data

7 Interpretability, Model Inspection and Representation Analysis

8 Advanced Deep Learning Methods

9 Implementation Tips

10 Conclusion