Machine Learning is an emerging technology trend that is changing the business landscape. Several industries such as computer, retail and finance benefit from machine learning. Machine learning has several advantages, but it can be difficult to take advantage of the technology. This is precisely where Google’s TensorFlow comes to the rescue. TensorFlow is an open source library developed by the Google Brain team. It facilitates the processing of computational and machine learning algorithms on a large scale.
Google’s TensorFlow makes it easy to use and implement machine learning solutions. The open source software library is designed to design high-performance numerical calculations and enterprise-level learning implementations. The structure supports multiple platforms and devices.
How it works?
TensorFlow helps developers create data flow graphs, which can explain moving data in a graph or series of n = processing nodes. Each node of the series represents a mathematical operation. Nodes connected by means of multidimensional matrices are called tensors.
There are many books, tutorials, projects, videos and other resources to learn TensorFlow. Here are the main resources you can refer to:
TensorFlow tutorials are available online and have been designed by the TensorFlow team. They start by helping you train your first neural network based on image classification and move toward using tf.keras, a high-level API used to create and train models. The tutorials also contain advanced text classification, regression and other concepts.
Stanford TensorFlow Course for Research in Deep Learning
A TensorFlow course for in-depth learning from Stanford University allows you to download notes and slides about Tensorflow for deep learning research. The entire course is technology-based, which allows users to gain a basic understanding of TensorFlow.
From TensorFlow Giancarlo Zaccone
This is one of the best books to learn the TensorFlow engine. The book begins with an introduction to the basics of TensorFlow, followed by details on creating programs using the framework. The book can also help you solve mathematical concepts, machine learning and in-depth learning problems.