Recurrent Neural Networks (RNN) Tutorial | Tensorflow Tutorial | Edureka | Deep Learning Rewind – 3


🔥Edureka Tensorflow Training –
This Edureka Recurrent Neural Networks tutorial video (Blog: ) will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story

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.

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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
3. Business Analysts
4. Information Architects
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies

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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.

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

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