From navigating to a new place to picking out new music, AI backed algorithms have laid the foundation for much of modern life.

In this article, get a higher-level view of Google’s TensorFlow deep learning framework, with the ultimate goal of helping you to understand and build your own deep learning algorithms from scratch.

Over the past couple of decades, deep learning has evolved rapidly, leading to massive disruption in a range of industries and organizations. The term was coined in 1943 when Warren McCulloch and Walter Pitts created a computer model based on neural networks of a human brain, creating the first artificial neural networks (or ANNs). Deep learning now denotes a branch of machine learning that deploys data-centric algorithms in real-time.

Google introduced Tensor Processing Units or TPUs in four years ago.

TPUs, unlike GPUs, were custom-designed to deal with operations such as matrix multiplications in neural network training.

Here’s a great beginner’s guide to the technology.

Google TPUs can be accessed in two forms — cloud TPU and edge TPU. Cloud TPUs can be accessed from Google Colab notebook, which provides users with TPU pods that sit on Google’s data centres. Whereas, edge TPU is a custom-built development kit that can be used to build specific applications. In the next section, we will see the working of TPUs and its key components.

Linear regression is likely the first algorithm that you would learn when starting down a career path  in data science or AI, because it’s simple to implement and easy to apply in real-time.

Here’s a great primer on how to do linear regression in TensorFlow 2.0.

This algorithm is widely used in data science and statistical fields to model the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). Several types of regression techniques are available based on the data being used. Although linear regression involves simple mathematical logic, its applications are put into use across different fields in real-time. In this article, we’ll discuss linear regression in brief, along with its applications, and implement it using TensorFlow 2.0.