Why TensorFlow?

TensorFlow is an open-source machine learning framework that is accessible to all programmers. Machine learning and deep learning applications are implemented using it. TensorFlow was designed by the Google team to help them develop and investigate fascinating artificial intelligence ideas. Because TensorFlow is written in Python, it is a simple framework to grasp.

TensorFlow is a Google-developed software library or framework for quickly implementing machine learning and deep learning principles. It combines computational algebra and optimization approaches to make many mathematical equations simple to calculate.

TensorFlow has a lot of machine learning libraries and is well-documented. It provides a few key functions and ways for doing so.

TensorFlow is sometimes referred to as a "Google" product. A wide range of machine learning and deep learning algorithms are included. For handwritten digit classification, image recognition, word embedding, and the generation of other sequence models, TensorFlow can train and run deep neural networks.

TensorFlow Python:

To give a specific example, AI can help Google users have a faster and more refined search experience. When a user types a keyword into Google's search field, the search engine makes a suggestion for the next word. Google intends to employ machine learning to make the most of their vast datasets in order to provide the best possible experience for their users. Machine learning is used by three different groups:

• Researchers

• Data Scientists

• Programmers

They may all collaborate and enhance their efficiency by using the same tools.

TensorFlow was built to scale since Google has more than just data; they also have the world's most powerful computer. TensorFlow is a machine learning and deep neural network research library created by the Google Research Team.

It was designed to run on a variety of CPUs, GPUs, and even mobile operating systems, and it includes wrappers in Python and C++.

TensorFlow Components:

Tensor:

TensorFlow gets its name from its underlying framework, Tensor. Tensors are used in every computation of TensorFlow. A tensor is an n-dimensional vector or matrix that can represent any type of data. A tensor's values all have the same data type with a known (or partially known) shape. The dimensionality of the matrix or array determines the shape of the data.

A tensor might come from either the input data or the output of a computation. All operations in TensorFlow are carried out within a graph. The graph is a series of computations that happen in order. Each operation is referred to as an op node, and they are all linked together.

The graph depicts the operations and relationships that exist between the nodes. It does not, however, show the values. The tensor, or a mechanism to populate the operation with data, is the edge of the nodes.

Graphs:

A graph framework is used by TensorFlow. The graph collects and explains all of the training's series computations. The graph offers a lot of benefits:

• It was designed to work on many CPUs or GPUs, as well as on mobile devices.

• The graph's portability enables the computations to be saved for immediate or later usage. The graph can be saved and run at a later time.

• The graph's computations are all done by joining tensors together.

TensorFlow Features:

• It features a feature that uses multi-dimensional arrays called tensors to design, optimize, and calculate mathematical expressions.

• Deep neural networks and machine learning approaches are supported by programming.

• It has a highly scalable compute capability that works with a variety of data sets.

• TensorFlow automates management by utilizing GPU computation. It also has a one-of-a-kind feature that optimizes the utilization of the same memory and data.

TensorFlow Keras:

Keras Python is a high-level Python library that runs on top of the TensorFlow framework and is small and easy to learn. It was created with the goal of better comprehending deep learning approaches, such as layering neural networks while keeping shape notions and mathematical intricacies. The following two types of framework creation exist:

• Sequential API

• Functional API

Consider these steps to build a deep learning model in Keras. 

• The data is being loaded.

• Pre-process the data that has been loaded.

• The model is defined.

• The model is being put together.

• Fit the model you've chosen.

• Evaluate it.

• Make the necessary adjustments.

• The model should be saved.

Installation of TensorFlow:

It is necessary to have "Python" installed on your machine in order to install TensorFlow. Python versions 3.4 and up are recommended for TensorFlow installation.

To install TensorFlow on a Windows computer, follow the steps below.

Step 1: Make sure you have the latest version of Python installed.

Step 2: To install TensorFlow in the system, a user can utilize any mechanism. "pip" and "Anaconda" are two words we recommend. Pip is a Python command for executing and installing modules.

We must first install the Anaconda framework on our PC before we can install TensorFlow.

Check in the command prompt with the "conda" command after the installation is complete. The command's execution is seen below.

Step 3: To start the TensorFlow installation, type the following command.

conda create --name tensorflow python = 3.5

It downloads the essential packages for TensorFlow installation.

Step 4: After completing the environmental configuration, the TensorFlow module must be activated.

activate tensorflow

Step 5: Install "Tensorflow" in the system with pip. The command that was used to install the software is listed below.

pip install tensorflow

And,

pip install tensorflow-gpu

After a successful installation, it's crucial to understand TensorFlow's sample program execution.

The following example demonstrates how to create a "Hello World" program in TensorFlow.

The code for the first programme

>> activate tensorflow

>> python (activating python shell)

>> import tensorflow as tf

>> hello = tf.constant(‘Hello, Tensorflow!’)

>> sess = tf.Session()

>> print(sess.run(hello))