TensorFlow is an end-to-end open source platform for machine learning. TensorFlow is a rich system for managing all aspects of a machine learning system; however, this class focuses on using a particular TensorFlow API to develop and train machine learning models.
See the TensorFlow documentation for complete details on the broader TensorFlow system.
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.
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It includes a feature of that defines, optimizes and calculates mathematical expressions easily with the help of multi-dimensional arrays called tensors.
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It includes a programming support of deep neural networks and machine learning techniques.
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It includes a high scalable feature of computation with various data sets.
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TensorFlow uses GPU computing, automating management. It also includes a unique feature of optimization of same memory and the data used.
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.
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.
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.
We encourage you to install Anaconda framework in our system.
After successfully nstalling Anaconda run the following commmands to install Tensorflow. Make sure anaconda environment, base, is activated before running the command below.
pip install tensorflow
or
conda install tensorflow 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.
>> import tensorflow as tf
>> hello = tf.constant(‘Hello, Tensorflow!’)
>> sess = tf.Session()
>> print(sess.run(hello)) In this week, you’ll learn how to define constants and variables, perform tensor addition and multiplication, and compute derivatives.
Knowledge of linear algebra will be helpful, but not necessary.
Learning materials and folder.
In this chapter, you will learn how to build, solve, and make predictions with models in TensorFlow 2. You will focus on a simple class of models – the linear regression model – and will try to predict housing prices. By the end of the chapter, you will know how to load and manipulate data, construct loss functions, perform minimization, make predictions, and reduce resource use with batch training. View chapter details