Keras vs Tensorflow vs PyTorch | Deep Learning Frameworks Comparison | Edureka

** AI & Deep Learning with Tensorflow Training: **
This Edureka video on “Keras vs TensorFlow vs PyTorch” will provide you with a crisp comparison among the top three deep learning frameworks. It provides a detailed and comprehensive knowledge about Keras, TensorFlow and PyTorch and which one to use for what purposes. Following topics will be covered in this video:
1:06 – Introduction to keras, Tensorflow, Pytorch
2:13 – Parameters of Comparison
2:18 – Level of API
3:06 – Speed
3:28 – Architecture
4:03 – Ease of Code
4:27 – Debugging
4:59 – Community Support
5:19 – Datasets
5:37 – Popularity
6:14 – Suitable use cases

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Check our complete Deep Learning With TensorFlow playlist here:
#keras #tensorflow #pytorch #deeplearning #machinelearning #frameworks
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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.

Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, please write back to us at or call us at IND: 9606058406 / US: 18338555775 (toll-free).


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  1. Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Deep Learning Certification Training Curriculum, Visit our Website:

  2. Pytorch is strong and elegant

  3. PyTorch is the best but the library is 500mb+

  4. Importing tensorflow takes time on my Orange Pi Zero. While pytorch is faster.

  5. Keras is love. Nice video @ edureka

  6. I want to learn machine learning but I don't know python what I di

  7. Keras worked fine for my audio signal.

  8. Difference between tensorflow and pytorch?

  9. Latest info:
    1. Keras is now a part of TensorFlow. No need to import Keras separately. Use Keras as follows:
    import tensorflow as tf
    xxx = tf.keras.yyy # Where yyy is a Keras function
    2. TensorFlow 2.0 Alpha release is now available. You can now start developing with 2.0, but beware that it will change. For now, it's best to use Virtualenv (or Docker, etc.) to keep it separate.
    3. For now, PyTorch is best for developing concepts and prototyping, but TensorFlow is best for a production system. This may change with time.

  10. I really love hearing adventure time's BMO teaching machine learning 😍

  11. Nice video 👌😍 thanks

  12. Thank you for posting – trying to learn

  13. i will go with tensorflow

  14. Only pytorch. You won't get freedom like you get in pytorch.

  15. Tensorflow and keras both popular and also they have same now ,right

  16. Thanks for this video edureka..😘😍

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