This presentation provides an overview of artificial intelligence (AI) and deep learning. It begins with introductions to AI and deep learning, explaining that AI allows machines to perform tasks typically requiring human intelligence through machine learning. Deep learning is a type of machine learning using artificial neural networks inspired by the human brain. The presentation then discusses why AI has grown recently, citing increased computing power, data storage, and data availability. It also covers deep learning model development and concepts like underfitting and overfitting. The presentation describes different types of learning approaches like supervised, unsupervised, and reinforcement learning. It concludes with popular applications of deep learning like precision agriculture, computer vision, and recommendations.

1. AI and Deep
Learning
Presented By : Manoj Kumar

2. Agenda Style
Overview of AI and deep learning, Why AI has taken off now
and Motivation behind Deep learning Neural Network
Introduction of AI and Deep Learning01
Development of DL model and concept of Under
fitting and overfitting
Technical Overview02
Supervised Learning, Unsupervised learning and
reinforcement learning
Types of Learning Approaches03
Major application of Deep Learning and main area
where Deep learning can applied prominently.
Application04

3. A R T I F I C I A L
I N T E L L I G E N C E
INTRODUCTION
[1]The field of artificial intelligence is essentially when machines can do
tasks that typically require human intelligence. It encompasses machine
learning, where machines can learn by experience and acquire skills
without human involvement. Deep learning is a subset of machine
learning where artificial neural networks, algorithms inspired by the
human brain, learn from large amounts of data. Similarly to how we
learn from experience, the deep learning algorithm would perform a
task repeatedly, each time tweaking it a little to improve the outcome.
DEEP LEARNING

4. New APIs and Algorithms
[2]Release/development of new algorithms, APIs and
Platforms for Deep Learning Applications
Computation Power
[2]More Computing Power is
available due to coming of NVIDIA
GPUs
Storage
[2]Availability of large and fast storage
devices in market e.g. SSD
Amount of Data
Why AI has
taken off
now
[2]Availability of Data has increased due to explosion
in Smart Mobiles and devices

5. Motivation : Deep Learning
MOTIVATION:
Create an Artificial Neural to solve problems
the same way a human brain does

6. A Deep Neural Network

7. Deep Learning Model development

8. Under-fitting and Over-fitting

9. Learning Approaches
[3]Learning based on
Feedback or Reward
Reinforcement Learning:
[3]Learn from a label training
data set which provide desired
output for given input
Supervised Learning: Three types :
[3]Discovering pattern in
unlabeled data when no output
values are given
Unsupervised Learning:

10. Deep Learning Application
Few Popular Applications: [4]Precision Agriculture, Video Captioning, Exploring Patterns from Satellite images, Image
detection in Healthcare, Creating Art and Music, Recommendations, behavior prediction,

11. Three main areas where DL is being prominently applied

12. References
[1] https://www.forbes.com/sites/bernardmarr/2018/10/01/what-is-deep-learning-ai-a-simple-
guide-with-8-practical-examples/#31c657028d4b
[3] https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0
[4] https://en.wikipedia.org/wiki/Deep_learning
[2] https://blog.wecognize.com/blog/2018/04/11/artificial-intelligence-booming-now/

13. ANY
QUESTIONS?