https://data-flair.training/blogs/tensorflow-tutorial/

1. TENSORFLOW
TUTORIAL
TENSORFLOW TUTORIAL FOR BEGINNERS
DATAFLAIR

2. TENSORFLOW TUTORIAL FOR
TENSORFLOW TUTORIAL FOR
TENSORFLOW TUTORIAL FOR
BEGINNERS
BEGINNERS
BEGINNERS
TODAY, IN THIS TENSORFLOW TUTORIAL FOR BEGINNERS, WE
WILL DISCUSS THE COMPLETE CONCEPT OF TENSORFLOW.
MOREOVER, WE WILL START THIS TENSORFLOW TUTORIAL WITH
THE HISTORY AND MEANING OF TENSORFLOW.
ALSO, WE WILL LEARN ABOUT TENSORS & USES OF TENSORFLOW.
WE WILL ALSO SEE TENSORFLOW EXAMPLES, FEATURES,
ADVANTAGES, AND LIMITATIONS. AT LAST, WE WILL SEE
TENSORBOARD IN TENSORFLOW.

3. TENSORFLOW
TUTORIAL – HISTORY
BEFORE THE UPDATION, TENSORFLOW IS KNOWN AS
DISTBELIEF. IT BUILT IN 2011 AS A PROPRIETARY
SYSTEM BASED ON DEEP LEARNING NEURAL
NETWORKS. THE SOURCE CODE OF DISTBELIEF WAS
MODIFIED AND MADE INTO A MUCH BETTER
APPLICATION BASED LIBRARY AND SOON IN 2015
CAME TO BE KNOWN AS TENSORFLOW.

4. WHAT IS TENSORFLOW?
WHAT IS TENSORFLOW?
WHAT IS TENSORFLOW?
TENSORFLOW IS A POWERFUL DATA FLOW-ORIENTED MACHINE LEARNING
LIBRARY CREATED BY THE BRAIN TEAM OF GOOGLE AND MADE OPEN
SOURCE IN 2015. IT IS DESIGNED TO BE EASY TO USE AND WIDELY
APPLICABLE TO BOTH NUMERIC AND NEURAL NETWORK-ORIENTED
PROBLEMS AS WELL AS OTHER DOMAINS.
BASICALLY, TENSORFLOW IS A LOW-LEVEL TOOLKIT FOR DOING
COMPLICATED MATH AND IT TARGETS RESEARCHERS WHO KNOW WHAT
THEY’RE DOING TO BUILD EXPERIMENTAL LEARNING ARCHITECTURES, TO
PLAY AROUND WITH THEM, AND TO TURN THEM INTO RUNNING SOFTWARE.

5. TENSORFLOW TUTORIAL –
TENSORS
Now, as the name suggests, it provides primitives for defining functions on tensors
and automatically computing their derivatives.
Tensors are higher dimensional arrays that are used in computer programming to
represent a multitude of data in the form of numbers.
There are other n-d array libraries available on the internet like Numpy but
TensorFlow stands apart from them as it offers methods to create tensor functions
and automatically compute derivatives. There are other n-d array libraries available
on the internet like Numpy but TensorFlow stands apart from them as it offers
methods to create tensor functions and automatically compute derivatives.
Now, let’s see some more uses of Tensorflow in this Tensorflow Tutorial.
PAUCEK AND LAGE

6. Tensorflow Tutorial – Tensors

7. Tensorflow Tutorial – Tensorflow Ecosystem

8. TENSORFLOW HAS A RESPONSIVE CONSTRUCT AS YOU CAN EASILY VISUALIZE EACH
AND EVERY PART OF THE GRAPH.
IT HAS PLATFORM FLEXIBILITY, MEANING IT IS MODULAR AND SOME PARTS OF IT
CAN BE STANDALONE WHILE THE OTHERS COALESCED.
IT IS EASILY TRAINABLE ON CPU AS WELL AS GPU FOR DISTRIBUTED COMPUTING.
TENSORFLOW HAS AUTO DIFFERENTIATION CAPABILITIES WHICH BENEFIT
GRADIENT-BASED MACHINE LEARNING ALGORITHMS MEANING YOU CAN COMPUTE
DERIVATIVES OF VALUES WITH RESPECT TO OTHER VALUES WHICH RESULTS IN A
GRAPH EXTENSION.
ALSO, IT HAS ADVANCED SUPPORT FOR THREADS, ASYNCHRONOUS COMPUTATION,
AND QUEUES.
IT IS CUSTOMIZABLE AND OPEN SOURCE.
THE FOLLOWING ARE THE ADVANTAGES OF THE TENSORFLOW TUTORIAL:
TENSORFLOW TUTORIAL
– ADVANTAGES

9. CONCLUSION
Hence, in this TensorFlow tutorial, we saw
what is TensorFlow, and how it works.
Moreover, we discussed the history and
features of TensorFlow. Along with this, we
discussed the TensorFlow example and its
advantages.
Moreover, we learned about Tensors and
TensorBoard. Still, if any doubt regarding the
TensorFlow tutorial, ask in the comment tab.