# Tensorflow Tutorial for Beginners | What is TensorFlow

## 1. Tensorflow Tutorial – Objective

Today, in this TensorFlow tutorial for beginners, we will discuss the complete concept of TensorFlow. Moreover, we will start this TensorFlow tutorial with history and meaning of TensorFlow. Also, we will learn about Tensors & uses of TensorFlow. Along with this, we will see TensorFlow examples, **features**, advantage, and limitations. At last, we will see TensorBoard in TensorFlow.

So, let’s start TensorFlow Tutorial.

**Do you know about TensorFlow Installation**

## 2. TensorFlow Tutorial – History

Before the updation, TensorFlow is known as Distbelief. It built in 2011 as a proprietary system based on* deep learning neural networks*. The source code of distbelief was modified and made into a much better application based library and soon in 2015 came to be known as TensorFlow.

## 3. What is Tensorflow?

TensorFlow is a powerful data flow oriented **machine learning **library created the Brain Team of Google and made open source in 2015. It is designed to be easy to use and widely applicable to both numeric and neural network oriented problems as well as other domains.

Basically, TensoFlow is a low-level toolkit for doing complicated math and it targets researchers who know what they’re doing to build experimental learning architectures, to play around with them and to turn them into running software.

Generally, it can think of as a programming system in which you represent computations as graphs. *Nodes* in the graph represent math operations, and the edges represent multidimensional data arrays (tensors) communicated between them.

## 4. TensorFlow Tutorial – Latest Release

The latest release of TensorFlow is 1.7.0 and is available on www.tensorflow.org. It designes with *deep learning* in mind but it is applicable to a much wider range of problems.

**Learn MNIST Dataset and Softmax Regression**

Next, let’s know more about Tensor in this Tensorflow Tutorial.

## 5. TensorFlow Tutorial – Tensors

Now, as the name suggests, it provides primitives for defining functions on tensors and automatically computing their derivatives.

Basically, tensors are higher dimensional arrays which are used in computer programming to represent a multitude of data in the form of numbers. There are other n-d array libraries available on the internet like Numpy but TensorFlow stands apart from them as it offers methods to create tensor functions and automatically compute derivatives.

There are other n-d array libraries available on the internet like *Numpy* but TensorFlow stands apart from them as it offers methods to create tensor functions and automatically compute derivatives.

**Read about Recurrent Neural network**

Now, let’s see some more uses of Tensorflow in this Tensorflow Tutorial.

## 6. TensorFlow Tutorial – Uses of TensorFlow

You can build other **machine learning algorithms** on it as well such as decision trees or k-Nearest Neighbors. Given below is an ecosystem of Tensorflow :

As can be seen from the above representation, TensorFlow integrates well and has dependencies that include *GPU* processing, python and Cpp and you can use it integrated with container software like docker as well.

Next, in Tensorflow Tutorial, we will see the concept of TensorBoard.

## 7. TensorFlow Tutorial – TensorBoard

**TensorBoard**, a suit of visualizing tools, is an easy solution to Tensorflow offered by the creators that lets you visualize the graphs, plot quantitative metrics about the graph with additional data like images to pass through it.

## 8. TensorFlow Tutorial – Operation

Tensorflow runs on a variety of platforms and the installation is Linux-only and more tedious than CPU-only installation. It can install using *pip* or *conda* environment. The applications go beyond deep learning to support other forms of machine learning like reinforcement learning, which takes you into goal-oriented tasks like winning video games or helping a robot navigate an uneven landscape.

## 9. Tensorflow Applications

There are umpteen **applications of machine learning**. TensorFlow allows you to explore the majority of them including sentiment analysis, google translate, text summarization and the one for which it is quite famous for, **image recognition** which uses by major companies all over the world, including Airbnb, eBay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google, Facebook, Instagram, and even Amazon for various purposes. So, these all are TensorFlow Applications.

## 10. Tensorflow Tutorial – Features

Below, we are discussing the features of TensorFlow:

Tensorflow has **APIs** for Matlab, and C++ and has a wide language support. With each day passing by, researchers are working on making it more better and recently in the latest Tensorflow Summit, tensorflow.js, a javascript library for training and deploying **machine learning models** introduce and an open source browser integrates platform is available for use at playground.tensorflow.org where you can see the real-time changes that occur while changing the hyperparameters.

## 11. Tensorflow Tutorial – Advantages

Following are the **advantages of TensorFlow** tutorial:

- Tensorflow has a responsive construct as you can easily visualize each and every part of the graph.
- It has platform flexibility, meaning it is modular and some parts of it can be standalone while the others coalesced.
- It is easily trainable on CPU as well as GPU for distributed computing.
- TensorFlow has auto differentiation capabilities which benefit gradient based machine learning algorithms meaning you can compute derivatives of values with respect to other values which results in a graph extension.
- Also, it has advanced support for threads, asynchronous computation, and queues.
- It is a customizable and open source.

**Let’s explore TensorFlow Mandelbrot Set**

## 12. Tensorflow Tutorial – Limitations

- TensorFlow has GPU memory conflicts with Theano if imported in the same scope.
- No support for OpenCL
- Requires prior knowledge of advanced calculus and linear algebra along with a pretty good understanding of machine learning.

So, this was all on Tensorflow Tutorial. Hope you like our explanation.

## 13. Conclusion

Hence, in this TensorFlow tutorial, we saw what is TensorFlow, how it works. Moreover, we discussed history and features of TensorFlow. Along with this, we discussed TensorFlow example, advantages. Moreover, we learned about Tensors and TensorBoard. Still, if any doubt regarding TensorFlow tutorial, ask in the comment tab.

**See also –**

**Wide & Deep Learning with TensorFlow**

**For reference**