DEEP LEARNING DEMO

1. Deep Learning

2.  Understand why we need a new technique : Deep Learning in
AI (when we have other AI techniques).
 Understand the evolution of DL over time and the current
business interest in this technology.
Agenda

3. • Accuracy
(Example: speech processing technique using AI(75%),DL(85%))
• Image recognition error rate
(AI(25.7%),DL(16%))
Why deep learning?

4. Industrial case studies to understand how deep learning is enabling industry on large scale, achieve
efficiencies in cost, save time and is becoming a key business differentiator.
Case Study-1: Detection of Diabetic retinopathy using Deep Learning
Case Study-2: How can deep learning help your organization today?
 Deep learning gives them valuable, actionable insights without expensive surveys and delays.
 They use deep learning to improve operations using
predictive maintenance,
understand the level of customer satisfaction,
minimize risks using fraud detection system
The deep learning techniques has taken their clients closer to their customers.

5. Industrial case studies
Case Study-3: Malware Detection using Deep Learning
The followings are a few facts about the software malware problem.
 Annual Worldwide Economic Damages from malware exceed $13 Billion.
 At the median, organizations experience five malware events per year.
 Email is the most common vector for malware infection.
 Deep learning algorithms can work on the binaries and classify them as malicious or benign.
Case Study 4: Deep learning in Amazon Go
This system uses deep learning techniques.
The important point to note is that AI and deep learning is not only used to solve complex problems but also to simplify
our daily life.

7. Thank you