Introduction to TensorFlow Fundamentals

Introduction to TensorFlow Fundamentals

This is the first lab in the Machine Learning with TensorFlow series. The primary focus of this lab is to help you understand the fundamental components of TensorFlow.

Learning Objectives:

  • Creating scalars, vectors, matrices, and tensors
  • Inspecting different attributes of a tensor.
  • Performing basic operations on tensors.
  • Learn to index and slice tensors.
  • Reshaping and manipulating tensors.

To get the most out of this series:

  • you should know how to write code, ideally have some experience programming in Python.
  • have a decent understanding of variables, linear equations, matrices, calculus, and statistics.
  • don't just stick to the examples and code I've provided, play around and break things - that's the best way to learn.

Code/Environment Setup

In order to try out the code snippets or complete to-do tasks mentioned in each section, you can use Google Colaboratory notebook. Colab is basically Google's implementation of Jupyter Notebooks where you can run your code.

You can learn more about working with Colabs by following this notebook.

Next Steps!

Whoa! that was a good deep introduction to tensors. 

The next steps for you should be to dig into the documentation and try more methods. Or you can try the following:

  • If you want to start building, try to implement a basic perception of a neural network using tensorflow.
  • Use the automatic differentiation to learn how the linear regression model tries to fit a perfect line to the data.
  • Learn to use gradient tape. You can watch my video tutorial on the same.

Some useful resources to dive deeper:

I'll be rolling out the next lab on training your first neural network to solve a simple regression model.

Until then, keep those tensors flowing!