Skip to main content

Learn Tensorflow | Best Tensorflow Tutorial for Beginners

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.

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.
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.

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.
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.
Labels:

Comments

Post a Comment

Popular posts from this blog

Integrating Tensor flow API into ASP.net Web applications for Image detection

Introduction Tensor flow provides advanced Machine Learning API. For the example here we will provide a solution for the Image recognition. The same can be applied to other use cases such as Text recognition or any other data set that needs to solve the problems of AI and Machine Learning such as: Predictive Analytics Cognitive Processes and its Automation e.g. NLP Virtual Agents (Bot’s) Camera Image detection and processing e.g. Industrial Robotics and Drones Text Knowledge Analytics Application Development Steps In the first step we create an ASP.NET application for connecting the camera feed or input data. In the next step Install Tensor flow as per instructions on the website –>  Click here To begin using Tensor flow we need to decide on the model to use for processing input data – There is an option to use either ready-made models or develop on own depending on the problem at hand. For simplicity we will use ready-made model called a deep  convolut...
6 comments
Read more

Machine Learning with ML.NET 1.0

As a person coming from .NET world, it was quite hard to get into  machine learning  right away. One of the main reasons was the fact that I couldn’t start Visual Studio and  try out  these new things in the technologies I am proficient with. I had to solve another obstacle and learn other  programming languages  more fitting for the job like Python and R. You can imagine my happiness when more than a year ago,  Microsoft  announced that as a part of  .NET Core 3 , a new feature will be available –  ML.NET . In fact it made me so happy that this is the third time I write similar  guide . Basically, I wrote one when ML.NET was a  version 0.2  and one when it was  version 0.10 . Both times, guys from Microsoft decided to modify the  API  and make my articles obsolete. That is why I have to do it once again. But hey, third time is the charm, so hopefully I will not have to do this again until ML.N...
Post a Comment
Read more

Using Tensorflow Object Detection API to build a Toy detector

Here I extend the API to train on a new object that is not part of the COCO dataset. In this case I chose a toy that was lying around. See gif below. So far, I have been impressed by the performance of the API. The steps highlighted here can be extended to any single or multiple object detector that you want to build. Tensorflow Toy Detector~ You can find the code on my  Github  repo Collecting data The first step is collecting images for your project. You could download them from google ensuring you have a wide variation in angles, brightness, scale etc. In my case I created a video of the little aeroplane toy and used  Opencv  to extract images from the video. This saved me a lot of time. I ensured that images were taken from multiple angles. You can also randomly change brightness for some of the images so that the detector can work under different conditions of lightning. Overall 100–150 pics will suffice. See some sample images below: ...
Post a Comment
Read more