Python Tutorial : Constants and variables

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-tensorflow-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi! My name is Isaiah Hull and this is a course on the fundamentals of the TensorFlow API in Python. In our first video, we will briefly introduce TensorFlow and then discuss its two basic objects of computation: constants and variables. TensorFlow is an open-source library for graph-based numerical computation. It was developed by the Google Brain Team. It has both low and high level APIs. You can use TensorFlow to perform addition, multiplication, and differentiation. You can also use it to design and train machine learning models. TensorFlow two point zero brought with it substantial changes. Eager execution is now enabled by default, which allows users to write simpler and more intuitive code. Additionally, model building is now centered around the Keras and Estimator's high-level APIs. The TensorFlow documentation describes a tensor as "a generalization of vectors and matrices to potentially higher dimensions." Now, if you are not familiar with linear algebra, you can simply think of a tensor as a collection of numbers, which is arranged into a particular shape. As an example, let's say you have a slice of bread and you cut it into 9 pieces. One of those 9 pieces is a 0-dimensional tensor. This corresponds to a single number. A collection of 3 pieces that form a row or column is a 1-dimensional tensor. All 9 pieces together are a 2-dimensional tensor. And the whole loaf, which contains many slices, is a 3-dimensional tensor. Now that you know what a tensor is, let's define a few. We will start by importing TensorFlow as tf. We will then define 0-, 1-, 2-, and 3-dimensional tensors. Note that each object will be a tf dot Tensor object. If we want to print the array contained in that object, we can apply the dot numpy method and