This document provides an introduction to deep learning, including what deep learning is, its applications, a brief history from early neural networks to current uses by major companies, and basics like architecture, training processes, and common neural network types like CNNs and RNNs. It also discusses machine learning approaches, what deep learning learns from data, and tools for working with deep learning.

1. Deep learning
An introduction
Sumit Kumar
Ranvijay Krishna

2. Interest
Google trends Interest over period

3. Hype or reality?

4. What is artificial intelligence
Input :
Sensors Data
A I Output:
Movement Text

5. Machine learning basic introduction

6. ML basics
Learning Approaches

7. ML basics
Problem type

8. What is Deep Learning?

9. Inspired by the brain

10. Why deep learning?
Applications

11. A brief History
“One net to rule them all”

12. A brief History
“One net to rule them all”

13. The Big Players
Companies

14. The Big Player
Startups

15. DL- Basics
No More Feature Engineering

16. DL- Basics
Architecture

17. DL- Basics
What did it learn?

18. DL- Basics
ANN

19. DL- Basics
Neuron

20. DL- Basics
Non-linear activation function

21. DL- Basics
The training process

22. DL- Basics
CNN

23. DL- Basics
RNN

24. DL- Basics
Image Captioning- combining CNN and RNN

25. DL- Basics
NLP

26. DL- Basics
Usage requirements

27. DL- Tools

28. Outlook