TensorFlow is an open-source library used for deep learning, written in python.
It was released in 2018 by Google. TensorFlow basically does numerical computation using data flow graphs.
It is being used by many companies including Intel, Qualcomm, Nvidia, etc.
Now, let us get on the road to learn TensorFlow.
Prerequisites: Before we start learning TensorFlow, we must have knowledge of Python.
Step1:
Begin with its installation on your computer. Learn about Artificial Intelligence, machine learning, and deep learning.
Step2:
Understand the mathematical foundation of TensorFlow and its basics.
Step3:
Move on to some more important and interesting concepts. Learn convolutional neural networks, recurrent neural networks, TensorBoard visualization, and Word embedding.
Step4:
We are halfway done with it. Learn about single layer perception, linear regression, and CNN & RNN difference.
Step5:
Install TFLearn. Understand the concept of Keras, distributed computing, multi-layer perceptron training, and hidden layers of perceptron.
Step6:
Learn about optimizers in TensorFlow, exporting with TensorFlow, XOR implementation, gradient descent optimization.
Step7:
Almost done with TensorFlow, Understand the concepts of forming graphs and image recognition using TensorFlow.
Step8:
Whoa! We are done with it. Keep exploring to get a deep understanding of the concept.
