ANSARI - TensorFlow: Deep Learning and Artificial Intelligence (Machine Learning)

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TensorFlow

Deep Learning and Artificial Intelligence

Contents

Introduction

TensorFlow is a software library or framework, designed by the Google team to implement machine learning and deep learning concepts in the easiest manner. It combines the computational algebra of optimization techniques for easy calculation of many mathematical expressions.

The official website of TensorFlow is mentioned below

www.tensorflow.org

Let us now consider the following important features of TensorFlow

• It includes a feature of that defines, optimizes and calculates mathematical expressions easily with the help of multi-dimensional arrays called tensors.
• It includes a programming support of deep neural networks and machine learning techniques.
• It includes a high scalable feature of computation with various data sets.
• TensorFlow uses GPU computing, automating management. It also includes a unique feature of optimization of same memory and the data used.

Why is TensorFlow So Popular?

TensorFlow is well-documented and includes plenty of machine learning libraries. It offers a few important functionalities and methods for the same.

TensorFlow is also called a Google product. It includes a variety of machine learning and deep learning algorithms. TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embedding and creation of various sequence models.

Installation

To install TensorFlow, it is important to have Python installed in your system. Python version 3.4+ is considered the best to start with TensorFlow installation.

Consider the following steps to install TensorFlow in Windows operating system.

Step 1 Verify the python version being installed.

Step 2 A user can pick up any mechanism to install TensorFlow in the system. We recommend pip and Anaconda. Pip is a command used for executing and installing modules in Python.

Before we install TensorFlow, we need to install Anaconda framework in our system.

After successful installation, check in command prompt through conda command. The execution of command is displayed below

Step 3 Execute the following command to initialize the installation of TensorFlow

conda create --name tensorflow python = 3.5

It downloads the necessary packages needed for TensorFlow setup.

Step 4 After successful environmental setup, it is important to activate TensorFlow module.

activate tensorflow

Step 5 Use pip to install Tensorflow in the system. The command used for installation is mentioned as below

pip install tensorflow

And,

pip install tensorflow-gpu

After successful installation, it is important to know the sample program execution of TensorFlow.

Following example helps us understand the basic program creation Hello World in TensorFlow.

The code for first program implementation is mentioned below

>> activate tensorflow

>> python (activating python shell)

>> import tensorflow as tf

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

>> sess = tf.Session()

>> print(sess.run(hello))

Understanding Artificial Intelligence

Artificial Intelligence includes the simulation process of human intelligence by machines and special computer systems. The examples of artificial intelligence include learning, reasoning and self-correction. Applications of AI include speech recognition, expert systems, and image recognition and machine vision.

Machine learning is the branch of artificial intelligence, which deals with systems and algorithms that can learn any new data and data patterns.

Let us focus on the Venn diagram mentioned below for understanding machine learning and deep learning concepts.

Machine learning includes a section of machine learning and deep learning is a part of machine learning. The ability of program which follows machine learning concepts is to improve its performance of observed data. The main motive of data transformation is to improve its knowledge in order to achieve better results in the future, provide output closer to the desired output for that particular system. Machine learning includes pattern recognition which includes the ability to recognize the patterns in data.

The patterns should be trained to show the output in desirable manner.

Machine learning can be trained in two different ways

• Supervised training
• Unsupervised training

Supervised Learning

Supervised learning or supervised training includes a procedure where the training set is given as input to the system wherein, each example is labeled with a desired output value. The training in this type is performed using minimization of a particular loss function, which represents the output error with respect to the desired output system.

After completion of training, the accuracy of each model is measured with respect to disjoint examples from training set, also called the validation set.

The best example to illustrate Supervised learning is with a bunch of photos given with information included in them. Here, the user can train a model to recognize new photos.

Unsupervised Learning

In unsupervised learning or unsupervised training, include training examples, which are not labeled by the system to which class they belong. The system looks for the data, which share common characteristics, and changes them based on internal knowledge features.This type of learning algorithms are basically used in clustering problems.

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