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With the Deep Learning scene being dominated by three main frameworks, it is very easy to get confused on which one to use? In this video on Keras vs Tensorflow vs Pytorch, we will clear all your doubts on which framework is better and which framework should be used by beginners, intermediates and professionals.

The topics covered in this video are :

- 00:00:00 What is Keras, Tensorflow and Pytorch?
- 00:05:27 Differences beteen Keras, tensorflow and Pytorch
- 00:11:46 Which framework should you use?

#keras #tensorflow #pytorch #deep-learning

1612013729

With the Deep Learning scene being dominated by three main frameworks, it is very easy to get confused on which one to use? In this video on Keras vs Tensorflow vs Pytorch, we will clear all your doubts on which framework is better and which framework should be used by beginners, intermediates and professionals.

The topics covered in this video are :

- 00:00:00 What is Keras, Tensorflow and Pytorch?
- 00:05:27 Differences beteen Keras, tensorflow and Pytorch
- 00:11:46 Which framework should you use?

#keras #tensorflow #pytorch #deep-learning

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View more: https://www.inexture.com/services/deep-learning-development/

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#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

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We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.

#pytorch #tensorflow #keras #python #deep-learning

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Deep learning is a sub-branch of machine learning. The unique aspect of Deep Learning is the accuracy and efficiency it brings to the table. When trained with a vast amount of data, Deep Learning systems can match, and even exceed, the cognitive powers of the human brain. How do the two top deep learning frameworks, i.e., PyTorch and TensorFlow, compare?

This article outlines five factors to help you compare these two major deep learning frameworks.

Tensorflow is basically a programming language that is embedded within Python, as Sorrow Beaver notes. Tensorflow’s code gets ‘compiled’ into a graph by Python. It is then run by the TensorFlow execution engine. Pytorch, on the other hand, is essentially a GPU enabled drop-in replacement for NumPy that is equipped with a higher-level functionality to build and train deep neural networks.

With Pytorch, the code executes very fast, it is very efficient, and you will require no new concepts to learn. Tensorflow, on the other hand, requires concepts such as placeholders, Variable scoping as well as sessions.

Pytorch has a dynamic process of creating a graph. Graphs on PyTorch can be built by interpreting a line of code corresponding to the particular aspect of a graph.

Tensorflow, on the other hand, has a static process of graph creation that involves graphs going through compilation and running on the execution engine.

Pytorch code will use the standard Python debugger, unlike TensorFlow, where you will need to learn the TF debugger and request the variables from the session for inspection.

Tensorflow supports features such as:

- Fast Fourier transforms
- Checking a tensor for NaN and infinity
- Flipping a tensor along a dimension

These are features that Pytorch doesn’t have, but as it grows in popularity, the gap will definitely be bridged.

When comparing the two frameworks in serialization, TensorFlow’s graph can be saved as a protocol buffer, which includes operations and parameters. The TensorFlow graph can then be loaded in other programming languages, such as Java and C++. This is important, especially for deployment stacks, where Python is not an option.

Pytorch, on the other hand, has a simple API that can either pickle the entire class or save all weights of a model.

All in all, saving and loading models are simplified in these two frameworks.

#deep learning #artificial inteligence #tensorflow #pytorch #deep learning

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Machine learning and Deep learning both are the buzzwords in the tech industry. Machine learning and deep learning both are the subdivision of artificial intelligence technology. If we further breakdown, deep learning is a subdivision of machine learning technology.

If you are familiar with the basics of machine learning and deep learning, it is excellent news!

However, if you are new to the AI field, then you must be confused. What is the difference between machine learning and deep learning?

There is nothing to worry about. This article will explain the differences in easy to understand language.

Machine learning is a branch of technology that studies computer algorithms. These algorithms allow the system to learn from data or improve by itself through experience. Machine learning algorithms make predictions or decisions without being explicitly programmed.

#artificial intelligence #comparison #deep learning #machine learning #machine learning vs deep learning