Deep Learning from first principles in vectorized Python, R and Octave

Table of Contents

Preface 4 Introduction 6

1. Logistic Regression as a Neural Network - 8
2. Implementing a simple Neural Network - 23
3. Building a L- Layer Deep Learning Network - 48
4. Deep Learning network with the Softmax - 85
5. MNIST classification with Softmax - 103
6. Initialization, regularization in Deep Learning - 121
7. Gradient Descent Optimization techniques - 167
8. Gradient Check in Deep Learning - 197
9. Appendix A - 214
10. Appendix 1 – Logistic Regression as a Neural Network - 220
11. Appendix 2 - Implementing a simple Neural Network - 227
12. Appendix 3 - Building a L- Layer Deep Learning Network - 240
13. Appendix 4 - Deep Learning network with the Softmax - 259
14. Appendix 5 - MNIST classification with Softmax - 269
15. Appendix 6 - Initialization, regularization in Deep Learning - 302
16. Appendix 7 - Gradient Descent Optimization techniques - 344
17. Appendix 8 – Gradient Check - 405 References - 475