Artificial Intelligence, History of Artificial Intelligence, Artificial Intelligence Use Cases, Artificial Intelligence Applications, Ways of Achieving AI, Machine Learning, Deep Learning, Supervised and Unsupervised Learning, Classification Vs Prediction, TensorFlow, TensorFlow Graphs, History of TensorFlow, Companies using TensorFlow, Using Deep Q Networks to Learn Video Game Strategies, TensorFlow Use Cases, AI & Deep Learning with TensorFlow, How TensorFlow used today For more updates on Big Data, Cloud Computing, Data Analytics, Artificial Intelligence, IoT subscribe to http://www.mybigdataanalytics.in

1. Artificial
Intelligence =
ML + DL with
Tensor Flow
Dr. Kamal Gulati

2. Contents
• Artificial Intelligence
• History of Artificial Intelligence
• Artificial Intelligence Use Cases
• Artificial Intelligence Applications
• Ways of Achieving AI
• Machine Learning
• Deep Learning
• Supervised and Unsupervised
Learning
• Classification Vs Prediction
• TensorFlow
• TensorFlow Graphs
• History of TensorFlow
• Companies using TensorFlow
• Using Deep Q Networks to Learn Video
Game Strategies
• TensorFlow Use Cases
• AI & Deep Learning with TensorFlow
• How TensorFlow used today

3. Sci-Fi Movies that might Become Reality

4. The Terminator (Parts)

6. What is Artificial Intelligence?

7. History of Artificial Intelligence

8. Artificial Intelligence Use Cases

9. Artificial Intelligence Use Cases

10. Artificial Intelligence Applications

11. Artificial Intelligence Applications

12. Artificial Intelligence Applications

13. Artificial Intelligence Applications

14. Artificial Intelligence Applications

15. Artificial Intelligence Applications

16. Ways of
Achieving AI
Machine Learning1
Deep Learning2

17. Machine
Learning
Machine learning refers to the use of
algorithms to parse data, process and
learn from it, in order to make
predictions or determinations about
something.
One of the best application for machine
learning is computer vision: OCR, object
tracking, object recognition etc.

20. Deep
Learning
Deep learning is a subfield of
machine learning concerned with
algorithms inspired by the structure
and function of the brain called
artificial neural networks (ANNs)
Compared to older ML algorithms,
Deep Learning performs better with
a large amount of data

22. Deep Learning Process

23. Machine Learning and Deep Learning

24. Supervised and Unsupervised Learning
Supervised Learning
• All data has been labeled (supervised)
by an expert. Thanks to this labeling
process, we can help the network to
realise the difference between classes
(even though sometimes this does not
happen).
• Some techniques: NNs, SVM, etc.
Unsupervised Learning
• Our data are not labeled. Unsupervised
algorithms consider confidence measures
among samples in order to create
homogeneous clusters.
• Most famous technique:
Clustering (k-means,
hierarchical etc.)

25. Classification Vs Prediction
Classification
• Given an input observation,
classification is the problem of
identifying to which of a set of
categories (classes) the new observation
belongs.
• i.e: trafficsignals recognition,
emotion recognition etc.
Prediction
• Prediction refers to the problem of
estimating the behaviour of a
phenomenon by analysing the “previous
history”
• i.e: object tracking,
forecasting etc.

26. Tensor Flow

27. TensorFlow
TensorFlow is an open-source library for
numerical computation and machine
learning.
Its basic principle is simple: you build in
Python a graph of computation to perorm
and then TensorFlow runs it efficiently
using optimized C++ code.
TensorFlow supports computation across
multiple CPUs and GPUs

28. TensorFlor
Originally developed by Google Brain Team to
conduct machine learning and deep neural
networks research.
General enough to be applicable in a wide variety of
other domains as well .
Provides an extensive suite of functions and classes
that allow users to build various models from
scratch.
https://www.youtube.com/watch?v=MotG3XI2qSs

29. Tensor Flow Graphs
Running a Simple Graph
• Software that uses TensorFlow
is often divided into two phases:
graph building and execution.
• In order to evaluate this graph
we must run the session and all
its initialisers.
• TensorFlow supports
computation across multiple
x = tf.Variable(3,name=”x”)
y = tf.Variable(4,name=”y”) f
= x*x + 2*y + 5
sess = tf.Session()
sess.run(x.initializer)
sess.run(y.initializer)
res = sess.run(f) print
res
x = tf.Variable(3,name=”x”)
y = tf.Variable(4,name=”y”) f
= x*x + 2*y + 5
with tf.Session() as session:
x.initializer.run()
y.initializer.run()
result = f.eval()

30. History
Google Brain's second-generation system.
Version 1.0.0 was released on February 11, 2017.
can run on multiple CPUs and GPUs.
Available on 64-bit Linux, macOS, Windows, and mobile
computing platforms including Android and iOS.

31. What
TensorFlow
Python API
Portability
Flexibility
Visualization
Checkpoints

32. Companies using
TensorFlow
• Google
• OpenAI
• DeepMind
• Snapchat
• Uber
• Airbus
• eBay
• Dropbox
• A bunch of startups

33. Using Deep Q Networks to Learn Video Game Strategies

34. TensorFlow Use Cases

35. AI & Deep Learning with TensorFlow

36. AI & Deep Learning with TensorFlow

37. AI & Deep Learning with TensorFlow

38. AI & Deep Learning with TensorFlow

39. AI & Deep Learning with TensorFlow

40. How TensorFlow used today?

41. For more updates on Big Data, Cloud
Computing, Data Analytics, Artificial
Intelligence, IoT subscribe to
http://www.mybigdataanalytics.in