An Introduction To TensorFlow

Introduction

TensorFlow is an open-source machine learning framework that used to implement machine learning and deep learning applications. It is designed in Python programming language by the Google team in 2005. It is mostly used to develop AI-based projects that allow a computer to learn how to automatically detect complex problems and provide the best possible decisions or solutions. It also Provides various optimization techniques to solve the complex calculation of mathematical expressions easier which helps increase the productivity of the system.

It provides many machine learning libraries and deep learning algorithms that can help to train and run deep neural networks or natural language processing models, image recognition systems, word embedding, and the creation of various sequence models. It allows developers to easily build and deploy robust and scalable ML-powered applications. It allows developers to create dataflow graph structures that indicate the data moves through a series of processing nodes. Each node in the graph represents a large numerical computation or mathematical operation and it accepts data in the form of a multidimensional data array that is also called a tensor.

Tensor Data Structure

In TensorFlow language, Tensors are used as the data structures which defined as a multidimensional array and it represents the connections between the nodes in any flow diagram called the Data Flow Graph and We prepare graphs with many nodes and these are executed in the form of a session with the data in the tensor. We need two activities for building a Tensor i.e. Build an n-dimensional and Convert the n-dimensional array. 

Three parameters of the tensor are:−

• Rank: It is defined as a Unit of dimensionality within a tensor that is called rank and tt identifies the number of dimensions of the tensor. 
• Shape: It represents the number of rows and columns together in the diagram.
• Type: It represents the data type assigned to the elements.

Features of TensorFlow are:

• It helps to work with mathematical expressions and multi-dimensional arrays.
• It helps to work with deep neural networks and machine learning concepts to deal with complex problems.
• It helps in GPU/CPU computing where the same code can be executed on both architectures.
• It provides high scalability of computation across machines and huge data sets.

Conclusion

Nowadays, the TensorFlow platform is mainly used for Classification, Perception, Understanding, Discovering, Prediction, and Creation, which helps to achieve high productivity in the real world.