TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Python | Edureka

///TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Python | Edureka

TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Python | Edureka

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** Flat 20% Off (Use Code: YOUTUBE) TensorFlow Training – **
This Edureka TensorFlow Tutorial video (Blog: will help you in understanding various important basics of TensorFlow. It also includes a use-case in which we will create a model that will differentiate between a rock and a mine using TensorFlow.

Below are the topics covered in this tutorial:

1. What are Tensors?
2. What is TensorFlow?
3. TensorFlow Code-basics
4. Graph Visualization
5. TensorFlow Data structures
6. Use-Case Naval Mine Identifier (NMI)

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Check our complete Deep Learning With TensorFlow playlist here:

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How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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About the Course
Edureka’s Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

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Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

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Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).

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By |2019-11-29T00:15:10+00:00November 29th, 2019|Python Video Tutorials|38 Comments

38 Comments

  1. edureka! November 29, 2019 at 12:15 am - Reply

    Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Tensorflow Course curriculum, Visit our Website: http://bit.ly/2r6pJuI

  2. hari tester November 29, 2019 at 12:15 am - Reply

    COOL COURSE, CAN YOU SHARE ME THE CODE

  3. Heena Girdher November 29, 2019 at 12:15 am - Reply

    Helo sir. could you please help me in installation of tensorflow. I have checked on many websites, i am not getting solution anywhere. I am getting error unable to load native tensorflow libraries. Please help me in this regard.

  4. Dilnoza November 29, 2019 at 12:15 am - Reply

    very good !

  5. Thilanga Attanayake November 29, 2019 at 12:15 am - Reply

    Hi fantastic job! I’d be interested in the code and data set. Thank you

  6. pavan sai November 29, 2019 at 12:15 am - Reply

    May i get the dataset and code..?

  7. Padhiyarkunal alk November 29, 2019 at 12:15 am - Reply

    Bro,
    Excellent work.
    #Thank you

    #Bhai ekdum Badhiya samjaya…

  8. When Maths meets coding November 29, 2019 at 12:15 am - Reply

    value for time thanks edureka!!!

  9. Carey Main November 29, 2019 at 12:15 am - Reply

    Do you provide code somewhere?

  10. hab eang November 29, 2019 at 12:15 am - Reply

    Wow a clear and good Tutorial thank you

  11. Asela Kotagama November 29, 2019 at 12:15 am - Reply

    Thank you, sir! Can you give us the code and the dataset??

  12. Ihsan ali November 29, 2019 at 12:15 am - Reply

    Thank you for the explanations,
    Where i can find the lecture slides?

  13. Sourabh Yadav November 29, 2019 at 12:15 am - Reply

    Please provide the data and code for the same. Thanks.

  14. Pushpajit Biswas November 29, 2019 at 12:15 am - Reply

    We saw that the model predicts wrong two times, how can we get 100% accuracy, cuz you know it's about life depending on a machine, if machine says that it's a rock instead of a mine then you know what will happen, so how can we achive 100%

  15. Tamilarasan Janakiraman November 29, 2019 at 12:15 am - Reply

    Great video that! Highly recommended for begginers!

  16. raghotham p November 29, 2019 at 12:15 am - Reply

    Very interesting ! very inspiring ..Please provide me the code and Data set

  17. Ketan Bhenwal November 29, 2019 at 12:15 am - Reply

    Can you provide code please

  18. tanmay chanda November 29, 2019 at 12:15 am - Reply

    Can you provide the code?

  19. tiantai deng November 29, 2019 at 12:15 am - Reply

    Thanks a lot for the tutorial, I really learn something as a new player to tf

  20. Arnab Das November 29, 2019 at 12:15 am - Reply

    I need dataset and Code

  21. Misterlikeseverythin November 29, 2019 at 12:15 am - Reply

    Good tutorial, but the jump in complexity from first use cases to the example was a bit too extreme.

  22. My colourful world November 29, 2019 at 12:15 am - Reply

    i need the code and data sheet

  23. sharifa nowrozi November 29, 2019 at 12:15 am - Reply

    Thank u so much for your kind explaination …

  24. John V November 29, 2019 at 12:15 am - Reply

    Can I have access to the data set please?

  25. aiswarya manikandan November 29, 2019 at 12:15 am - Reply

    Amazing explanation!!

  26. Kevin Chahine November 29, 2019 at 12:15 am - Reply

    So amazing it makes sense now. You are such a great teacher. Thank you

  27. Sangramjit Chakraborty November 29, 2019 at 12:15 am - Reply

    A really really good tutorial. I implemented my first research paper after watching this. I recommend everyone who asks to watch this tutorial.

  28. Rames P November 29, 2019 at 12:15 am - Reply

    Simple and effective tutorial!

  29. Arif Mollick November 29, 2019 at 12:15 am - Reply

    How can i get the dataset??

  30. varma kakarlapudi November 29, 2019 at 12:15 am - Reply

    Excellent tutorial with clear cut explanation and sharing required subject. Can you please share code and data to my email.

  31. Zeyi Wang November 29, 2019 at 12:15 am - Reply

    Hi edureka!, could you explain how to add regularization and dropout option to this code?

  32. Future2020 November 29, 2019 at 12:15 am - Reply

    Nice tutorial, how to get the dataset and code?

  33. Sparsh Maheshwari November 29, 2019 at 12:15 am - Reply

    I need the code and the dataset

  34. Nadir Mustafa November 29, 2019 at 12:15 am - Reply

    Hello
    please how can i got the dataset of this tutorial

  35. The Total November 29, 2019 at 12:15 am - Reply

    Awesome tutorial, please give the dataset

  36. Abhishek Mirgude November 29, 2019 at 12:15 am - Reply

    Can I get data_set and code for the practise purpose ??

  37. mukesh santuka November 29, 2019 at 12:15 am - Reply

    Can you provide the source code and the dataset..

  38. UrAvgGamer November 29, 2019 at 12:15 am - Reply

    The tutorial is very good and up to the point. Team Edureka, Can you please share code and data set used. Thanks

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