The document discusses the importance of artificial intelligence (AI) and its integration into products, emphasizing the need for understanding machine learning (ML) and deep learning (DL) to avoid becoming obsolete. It covers neural networks, particularly convolutional neural networks (CNNs), which are designed to recognize visual patterns, and details their functionality including convolution, pooling, and the rectified linear unit (ReLU) activation function. Additionally, it highlights various applications of AI, such as virtual assistants and e-commerce recommendations.

1. Machine Learning
Introduction
by Manish Panchmatia
& Saurabh Bansal

2. • AI = New Electricity By Andrew NG
• What was meaning of CEO?
• Chief Engineering Officer
• Chief Electrical Officer
• New Product = Any Product + Electricity
• Now New Product = Any Product + AI

3. Some more Quotes about AI
• AI + Product means People should be able to interact
with computing in a natural and seamless way. – Sundar
Pichai
• The world's first trillionaire will be an AI entrepreneur –
Mark Cuban
• Predicting the future isn’t magic, it’s artificial intelligence.
- Dave Waters

4. AI Examples
Auto Tagging
Self Driving Car
Translation
E-commerce Recommendation
Gmail Smart Reply q
Windows Cortana , Apple Siri : Virtual Assistant
Prisma photo editor : Image to painting
https://www.youtube.com/watch?v=Y2VF8tmLFHw

5. Types of Data

6. AI ? ML ? DL ?
Artificial Intelligence,
deep learning, machine learning
— whatever you’re doing
if you don’t understand it
— learn it.
Because otherwise you’re going
to be a dinosaur
within 3 years. - Mark Cuban

7. AI – ML Techniques

8. Algorithm Cheat Sheet

9. ML WorkFlow

10. Data Analysis Tools & Languages

11. Machine Learning Basics

12. Neural Network Basics

13. CNN for Image
Classifications

15. Convolutional Neural Network
1. CNN is a feed-forward network that can extract topological properties
from an image.
2. Like almost every other neural networks they are trained with a version of
the back-propagation algorithm.
3. Convolutional Neural Networks are designed to recognize visual patterns
directly from pixel images with minimal preprocessing.
4. They can recognize patterns with extreme variability (such as handwritten
characters).

16. Deep Learning
1. Deep Learning can contain a number of layers of Neural Networks.
2. The input to each layer (two-dimensional arrays) looks a lot like the
output (two-dimensional arrays) from previous layer.
3. Each layer have a number of steps like Pooling, Convolution,
Rectification, Normalization, etc.

17. Convolution
Suppose we have a problem, we need to
select whether input image is a ‘X’ or ‘O’

18. Convolution
So we will get maximum outputs at the pixels, where the
pattern matches with filter pixels.

19. Pooling
Pooling involves stepping a small
window across an image and taking
the maximum / average value from
the window at each step called
Strides.
So 2 types of pooling:
- Average Pooling
- Max Pooling

20. Rectified Linear Units (ReLU)
1. ReLU is most used Activation
function in Neural Networks.
2. It’s math is very simple—
wherever a negative number
occurs, swap it out for a 0.
3. This helps the CNN stay
mathematically healthy.

21. Fully connected layers
1. Fully connected layers take the
high-level filtered images and
translate them into categories.

22. CNN with MNIST Dataset
https://github.com/Hvass-Labs/TensorFlow-
Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb

23. Thank You