Artificial Intelligence , Deep Learning Overview AI , ML& Deep Learning Why use Deep Learning Deep Learning & machine Learning Difference Frameworks TensorFlow Environment Model : Training , Deployment , Use Transfer Learning : Reusing Existing Model TensorFlow hub Learning Curve of Human and machine , the way of training

1. Deep Learning

2. Content Overview
1. Deep Learning Overview
1. AI , ML& Deep Learning
2. Why use Deep Learning
2. Deep Learning & machine Learning Difference
3. Frameworks
4. TensorFlow
5. Environment
6. Model : Training , Deployment , Use
7. Transfer Learning : Reusing Existing Model
8. TensorFlow hub
Content Overview

3. Content Not Covered
1. Machine Learning basics
2. Perceptron & , Weight , Bias & Activation function
3. Maths ( Probability , Modeling of Neural Network )
4. Comparison of Deep Learning Frameworks
5. Code Level Details
To be done later

4. 4
The Basics

5. Deep Learning Overview
A System which can Learn ( the relations/features ) & predict Like Humans
Learn Predict

6. How Human Learn
How a Human child learn to differentiate Apple & Orange
Few General steps
1. Teacher show the picture & tell them
2. Student Remember it
3. If teacher ask question , student tell the answer

7. Over the time , Children can perform the identification without teacher
How Human Learn

8. Even for new variety of Orange , which he has never seen
How Human Learn

9. What’s So Deep (and Powerful) About Deep Learning
Or new variety Apple , which he has never seen

10. How Human Learn
Training Act

11. How Human Learn
Training Predict

12. Training
Design
New
things
How Human Learn

13. Deep Learning
Training Model Predict

14. Neural Networks

15. 1
ML & DL Difference

16. Model
• We define a Neural Network
• & Train Neural Network to get
Models
• Based on Mathematical Principals
• For Prediction
Neural Networks

17. Model
• We define a Neural Network
• & Train Neural Network to get
Models
• Based on Mathematical Principals
• For Prediction
Neural Networks

18. Why use Deep Learning : To Solve Complex Problem

19. 1
Factors Affecting Growth of Deep Learning
Data Availability Cheap Computing Open Source Cloud Infra

20. 2
Applications of deep Learning

21. Data
2
Neural Network
Training
Model
Prediction
Neural Networks

22. Data
2
Neural Network
Training
Model
Prediction
Frameworks
Design Option
TensorFlow PyTorch Others

23. Data
2
Neural Network
Training
Model
Prediction
Frameworks
Design Option

24. Data
2
Neural Network
Training
Model
Prediction
Types of Neural Network
Different Problem : Different design approach
• Feed-Forward Network ( FNN )
• Recurrent Neural Network (RNN)
• Long Short Term Memory Network (LSTM)
• Convolutional Neural Network (CNN)
• Deconvolutional Neural Network (DNN)
• Generative Adversarial Network (GAN)
• Attention Model ( BERT )

25. Data
2
Neural Network
Training
Model
Prediction
Where to Train
Where to train Neural Network
CPU GPU TPU

26. Data
2
Neural Network
Training
Model
Prediction
Deployment
Where to deploy
• Almost all devices
• Train a Model at one Place & Utilize
in different place

27. Data
2
Neural Network
Training
Model
Prediction
Reuse Trained Model
• Training is expensive
• Model deployment at different platform
– Yes
• Utilize trained model for different task ?
– Yes
– Transfer Learning

28. Data
2
Neural Network
Training
Model
Prediction
Need to Use Existing Models
• Training is expensive
• Model deployment at different platform
– Yes
• Utilize trained model for different task ?
– Yes
– Transfer Learning

29. Framework

30. Frameworks
•Mainly used for:
•Classification,
•Perception
•Understanding
•Discovering
•Prediction
•Creation
TensorFlow : Open source deep learning library
•
• Released by Google in 2015
• >2200 Contributors
• 2.0 , Sep 2019
• 2.5 , May 14, 2021
• GPU / TPU support
• Python , C++ , CUDA

31. Why TensorFlow

32. Why TensorFlow
Easy
Simplified APIs.
Focused on Keras and
eager execution
Powerful
Flexibility and performance.
Power to do cutting edge
research and scale to > 1
exaflops
Scalable
Tested at Google-
scale.
Deploy everywhere

33. Deployment
Servers Edge devices JavaScript

34. TensorFlow
Trainings Deployment

35. Keras

36. Environment

37. Code
Beginners : Using Keras

38. Code
Expert

39. Transfer Learning
Reuse Trained Network for Different Task

40. Transfer Learning
Why :
Training is costly & time taking
Generic Features are same

41. Transfer Learning
Strategy
Use a already trained Network
Remove its last layer
Retrain

42. Transfer Learning
Strategy
Use a already trained Network
From Where
: TensrFlow Hub

43. Transfer Learning

44. Transfer Learning

45. Transfer Learning

46. Transfer Learning