Introduction to Machine Learning with TensorFlow

The Google Brain team created TensorFlow, an open-source library. It was designed for activities that need a lot of numerical computations.

TensorFlow was designed specifically for machine learning and deep learning networks. TensorFlow ran faster than python code thanks to the use of C/C++ as a backend.

A data flow graph is a standard programming model for parallel processing that is used by tensor flow applications.

Two computational units are present in data flow graphs.

That’s what nodes and edges are.

The mathematical operations are represented by nodes. Multidimensional arrays are indicated by edges (Tensors)

1) Tensorflow provides python and C++ APIs (Python API is easier to use)

2) Great compilation time in comparison to the alternative deep learning libraries.

3) Support CPUs, GPUs, and distributed processing.

Tensors are the types of data that are exchanged between operations. It’s a multidimensional array, which means it may hold scalar values, vector values, a two-dimensional matrix, and so on.

Let’s look at an image as an example. The dimensions of an image are its height, breadth, and colors. In this scenario, the multidimensional array is well suited to storing the bits of data.

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