Tensorflow Tutorial for Beginners.pdf
- 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.