#MachineLearning #DeepLearning #ML #AI #ArtificialIntelligence #TensorFlow
2) Text
Based Applications
4) Video
Detection
Nasa ( National Aeronautics and Space
Administration) is designing a system with TensorFlow for orbit
classification and object clustering of asteroids. As a result, they can
classify and predict NEOs (near earth objects). ( So, in a way TensorFlow
is life-saving!!! )
Beginner’s
guide to Tensorflow
Did you know TensorFlow is Life-Saving ? Read- on
INTRODUCTION:
The primary software tool of Deep Learning
is TensorFlow. It is an open source artificial intelligence library, using data
flow graphs to build models.
It allows
developers to create large-scale neural networks with many layers.
USED
FOR:
TensorFlow is mainly used for:
1)
Voice/Sound Recognition
2) Text
Based Applications
3)
Image Recognition
4) Video
Detection
INTERESTING
FACTS :
Nasa ( National Aeronautics and Space
Administration) is designing a system with TensorFlow for orbit
classification and object clustering of asteroids. As a result, they can
classify and predict NEOs (near earth objects). ( So, in a way TensorFlow
is life-saving!!! )
NOW
TECHNICAL STUFF :
TensorFlow is
a library for numerical computation
where data flows through the graph.
Data in TensorFlow is
represented by n-dimensional arrays called Tensors.
Graph is made of data(Tensors) and mathematical operations.
§ Nodes on the graph:
represent mathematical operations.
§ Edges on the graph:
represent the Tensors that flow between operations.
There is one more
aspect in which TensorFlow is very different from any other programming
language.
In TensorFlow, you first need to create a blueprint of whatever you want
to create. While you are creating the graph, variables don’t have any value. Later
when you have created the complete graph, you have to run it inside a session, only
then the variables have any values.
import
tensorflow as tf
Graph
in TensorFlow:
GRAPH is the backbone of TensorFlow and every
computation/operation/variables reside on the graph. Everything that happens in
the code, resides on a default graph provided by TensorFlow. You can access
this graph by:
graph = tf.get_default_graph()
Next
big thing, Session!
A GRAPH is used
to define operations, but the operations can only run within a SESSION. Graphs and sessions
are created independently of each other.
Sess = tf.Session()
Tensors
in Tensorflow:
TensorFlow holds Data in tensors
i)
Constants
are constants whose value can’t be changed. You can
declare a constant like this:
a=tf.constant(1.0)
ii)
Variables
are again Tensors which are like variables in any
other language.
b=tf.Variable(2.0,name=None)
iii)
PlaceHolders
are tensors which are waiting to be initialized/fed.
Placeholders are used for training data which is only fed when the code is
actually run inside a session. What is fed to Placeholder is called feed_dict.
Feed_dict are key value pairs for holding data:
a = tf.placeholder("float")
