Introduction to TensorFlow

TensorFlow is an open-source neural network framework that makes it easy to build and deploy Machine Learning (ML) models across many different devices. In order to install TensorFlow, you’ll need to have Python installed first.

You can reference the following if you need some help getting TensorFlow installed:
https://www.tensorflow.org/install/pip#virtual-environment-install

I would strongly recommend installing TensorFlow in a virtual environment. Once that’s installed, if you’re comfortable with python you should be familiar with the concept of importing packages. One way of checking you have TensorFlow installed correctly is to try to install it while in interactive mode.

For example:

(venv) … $ python
…
>>> import tensorflow as tf

Well, don't stop there. Let's try to print the version:

>>> print(‘tensorflow version’,tf.__version__)
tensorflow version 2.2.0

How about add?

>>> print(tf.add(1,1))
tf.Tensor(2, shape=(), dtype=int32)

You good??? No vicious erros?? I’m impressed. You appear to be somewhat comfortable with TensorFlow, so I think next time we’ll be ready to talk about Keras, which is a high-level neural network built on top of TensorFlow.

References:

1. A LIGHTNING-FAST INTRODUCTION TO DEEP LEARNING AND TENSORFLOW 2.0.
   https://builtin.com/machine-learning/introduction-deep-learning-tensorflow-20. Last accessed: 6/11/2020.

2. TFRT: A new TensorFlow runtime.https://blog.tensorflow.org/2020/04/tfrt-new-tensorflow-runtime.html. Last accessed: 6/11/2020.

3. The Google’s 7 steps of Machine Learning in practice: a TensorFlow example for structured data. https://towardsdatascience.com/the-googles-7-steps-of-machine-learning-in-practice-a-tensorflow-example-for-structured-data-96ccbb707d77. Last accessed: 6/11/2020.