TensorFlow Tutorial for Beginners: Your Gateway to Building Machine Learning Models

From virtual assistants to self-driving cars, tech companies are in a race to launch products and enhance the user experience by exploring the capabilities of Artificial Intelligence (AI). It is evident from a Market Research Future Indeed report, which reveals that the machine learning jobs market is projected to be worth almost $31 billion by 2024. Machine learning creates algorithms that enable machines to learn and apply intelligence without being directed, and TensorFlow is an open-source library used for building machine learning models.

In this TensorFlow tutorial, we are going to cover the following topics:

• What is deep learning?

• Top libraries to develop deep learning applications

• What is TensorFlow?

• Why use TensorFlow?

• Building a computational graph

• Programming elements in TensorFlow

• Introducing Recurrent Neural Networks (RNN)

• Use case implementation of RNN using TensorFlow

  
  

What is Deep Learning?

Deep learning is a subset of machine learning. There are certain specialties in which we perform machine learning, and that's why it is called deep learning. For example, deep learning uses neural networks, which are like a simulation of the human brain. Deep learning also involves analyzing large amounts of unstructured data, unlike traditional machine learning, which typically uses structured data. This unstructured data could be fed in the form of images, video, audio, text, etc.

The term ‘deep’ comes from the fact that a neural network can have multiple hidden layers.