Entrepreneur and Technology for Students

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E N T R E P R E N E U R
S A N J E E V K O U S H I K
H T T P S : / / W W W. L I N K E D I N . C O M / I N / S A N J E E V K O U S H I K
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2. M AT H I S T H E K E Y
Differential Equations
• Sine Wave
• Linear Wave
• Fourier Series

3. T E C H P E O P L E D E V E L O P
• Hilbert Curve’s algorithm (defining pixels for the pictures you take from the
DSLR) can be helpful along with convolutional neural network algorithms
• if you’ve to study how images are built around pixels, that study is awesome!
• Spend time understanding the ZETA functions, Rotational Invariance, Linear
transformations, Determinants etc.,

4. C O M M E R C E & A R T S P E O P L E T E L L
• A team in a college had built an app around image processing and deep
learning to identify the pictures
• They meet a mentor and he asks them to be specific
• Then they brainstormed
• They come up with a focus point

5. C O M M E R C E & A R T S P E O P L E T E L L …
• The focus was on smiling faces
• One of the teammate’s sister had dimple cheeks and with her permission, they started
to analyze the photographs she had published in the public domain (like FB, Twitter),
they used APIs of these companies for search and found many interesting information
from it, they went to the same mentor with this data.
• Whenever the lady went outside either on vacation or for some high profile events,
she wore a color that was always common. They had analyzed about 370 pics of
hers using that time.
• That color was Pink
One of the teammate’s sister had dimple cheeks and with her permission, they started to analyze the photographs she had published in the public domain (like FB, Twitter), they used APIs of these companies for search and found many interesting information from it, they went to the same mentor with this data.
1. Whenever the lady went outside either on vacation or for some high profile events, she wore a color that was always common and that was Pink. They had analyzed about 370 pics of hers using that time.

6. C O M M E R C E & A R T S P E O P L E T E L L …
• Now, the mentor further challenged the team (they were ready to take it and
were enthusiastic about it), can you with your software help get the information
as to there are many people in the nation.
• They went back to their office and started the process, so, after a few iterations
and 3 months time for actually getting the results they had worked around
using the technology, they were able to come up with some of the inputs. They
had analyzed about one million pictures.
• They also segregated pics according to age, occasions etc.,

7. C O M M E R C E & A R T S P E O P L E T E L L …
• About 65% of the ladies had dimple cheeks, rest of them
you could assume it is boys.
• 77% of them liked vibrant colors to wear during
occasions
• Indian ladies who had dimple cheeks wore bright sarees
and black or red party wears during occasions
• indian ladies with dimple cheeks also wore more Gold
than any other ladies with dimple cheeks globally
• 87% of the Indian ladies with dimple cheeks were
working women
• 60% of the Indian ladies with dimple cheeks in the
picture analyzed were above 30
• 49% of them had iPhone in their hand
• 63% were holding Android phones (this percentage
included women holding both iPhone and Android)
• 7% had iPads with them in pictures
• 73% wore branded clothing and shoes
• 3% wore hairband
• 31% wore spectacles
• 2% had Apple Watch to show
• 54% had worn bindi or bangles or both
• 25% of them in the pics were in a house
• 41% of the pics were outdoors

8. C O M M E R C E & A R T S P E O P L E T E L L …
One of the teammate’s sister had dimple cheeks and with her permission, they started to analyze the photographs she had published in the public domain (like FB, Twitter), they used APIs of these companies for search and found many interesting information from it, they went to the same mentor with this data.
1. Whenever the lady went outside either on vacation or for some high profile events, she wore a color that was always common and that was Pink. They had analyzed about 370 pics of hers using that time.
• Now what do we do with this data?
• Well, that’s what people like me do helping entrepreneurs commercialize the
data they have and what they can do with it for their future.

9. N AT U R E
Is all about the surrounding
T H A N K YO U !