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EMPOWERING
THE
MACHINE
LEARNING
REVOLUTION
Communications
 Communication theory states that
communication involves a sender and a
receiver (or receivers) conveying
information through a communication
channel.
Human2Human
Technologies:
 Telephone
 Mobile
Human2Machine
Technologies:
 TV Remote Control
 Remote Car locking
Machine2Machine
Technologies:
 Thermostat
 Motion Sensor light
Why Machine Learning Now
 Big Data
One of the important reasons is the
volume of available data. Terabyte-
sized Big Data can be easily
accessed with few clicks.
 Computing Power
The second reason is the advanced
computing power, especially
using Graphics Processing Unit
(GPU). GPU is a
specialized electronic
circuit designed to rapidly
manipulate and alter memory to
accelerate the creation of images in
a frame buffer.
Google Tensor Processing Unit(TPU)
 A tensor processing unit (TPU) is an AI accelerator
application-specific integrated circuit (ASIC)
developed by Google specifically for neural network
machine learning.
 1st Generation
 The tensor processing unit was announced in 2016
at Google I/O.The chip has been specifically
designed for Google's Tensor Flow framework, a
symbolic math library which is used for machine
learning applications such as neural networks.
 2nd Generation
 The second generation TPU was
announced in May 2017.Google stated
the first generation TPU design was
memory bandwidth limited, and using 16
GB of High Bandwidth Memory, In the
second generation design increased
bandwidth to 600GB/s and performance
to 45 TFLOPS.
Pitfalls Of Machine Learning
 Designing
hardware
using last
year’s
models.
Video
Presentation on Research Paper Made
by
 Rabbia Nasir
 Erum Shammim
 Ifra Batool
 Sara Khalique
 Laiba Jamil
 Yusra Raees

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Empowering Machine Learning

  • 2. Communications  Communication theory states that communication involves a sender and a receiver (or receivers) conveying information through a communication channel.
  • 4. Human2Machine Technologies:  TV Remote Control  Remote Car locking
  • 7.  Big Data One of the important reasons is the volume of available data. Terabyte- sized Big Data can be easily accessed with few clicks.  Computing Power The second reason is the advanced computing power, especially using Graphics Processing Unit (GPU). GPU is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer.
  • 8. Google Tensor Processing Unit(TPU)  A tensor processing unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning.  1st Generation  The tensor processing unit was announced in 2016 at Google I/O.The chip has been specifically designed for Google's Tensor Flow framework, a symbolic math library which is used for machine learning applications such as neural networks.
  • 9.  2nd Generation  The second generation TPU was announced in May 2017.Google stated the first generation TPU design was memory bandwidth limited, and using 16 GB of High Bandwidth Memory, In the second generation design increased bandwidth to 600GB/s and performance to 45 TFLOPS.
  • 10.
  • 11. Pitfalls Of Machine Learning  Designing hardware using last year’s models.
  • 12. Video
  • 13. Presentation on Research Paper Made by  Rabbia Nasir  Erum Shammim  Ifra Batool  Sara Khalique  Laiba Jamil  Yusra Raees