What Is TensorFlow 2?

TensorFlow is an open-source distributed numerical computation framework released by Google. It’s mainly intended to alleviate the painful details of implementing a neural network (for example, computing derivatives of the loss function with respect to the weights of the neural network). TensorFlow takes this a step further by providing efficient implementations of such numerical computations using Compute Unified Device Architecture (CUDA), which is a parallel computational platform introduced by NVIDIA. The application programming interface (API) of TensorFlow shows that TensorFlow provides thousands of operations that make our lives easier.

TensorFlow was not developed overnight. This is a result of the persistence of talented, good-hearted developers and scientists who wanted to make a difference by bringing deep learning to a wider audience. If we’re interested, we can take a look at the TensorFlow code. Currently, TensorFlow has around 3,000 contributors, and it sits on top of more than 115,000 commits, evolving to be better and better every day.

Getting started with TensorFlow 2

Now let’s learn about a few essential components in the TensorFlow framework by working through a code example. Let’s write an example to perform the following computation, which is very common for neural networks:

$$h= sigmoid(W \cdot x+b)$$

This computation encompasses what happens in a single layer of a fully connected neural network. Here $W$ and ...