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Deep learning is a subset of Artificial Intelligence (AI), a field growing popularly over the last several decades. Deep learning and machine learning are part of the artificial intelligence family, though deep learning is also a subset of machine learning.

It imitates the human brain’s neural pathways in processing data, using it for decision-making, detecting objects, recognizing speech, and translating languages. It learns without human supervision or intervention, pulling from unstructured and unlabeled data.

Deep learning processes machine learning by using a hierarchical level of artificial neural networks, built like the human brain, with neuron nodes connecting in a web. While traditional machine learning programs work with data analysis linearly, deep learning’s hierarchical function lets machines process data using a nonlinear approach.

Keras, TensorFlow and Pytorch are the three most popular deep learning frameworks. Let’s learn in detail each of these three.

#keras #tensorflow #pytorch #python

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Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.

Which framework is better for us then?

This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.

Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.

Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.

- The performance of Keras is smooth on both CPU and GPU.
- Keras provides modularity, flexibility to code, extensibility, and has an adaptation for innovation and research.
- The pythonic nature of Keras makes it easy to explore and debug the code.

Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.

Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.

- From mobiles to embedded devices and distributed servers Tensorflow runs on all the platforms.
- Tensorflow is the enterprise of solving real-world and real-time problems like image analysis, robotics, generating data, and NLP.
- Developers are implementing tools for translation languages and the detection of skin cancers using Tensorflow.
- Major projects using TensorFlow are Google translate, video detection, image recognition.

#keras tutorials #keras vs tensorflow #keras #tensorflow

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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

1600333481

Deep learning is a subset of Artificial Intelligence (AI), a field growing popularly over the last several decades. Deep learning and machine learning are part of the artificial intelligence family, though deep learning is also a subset of machine learning.

It imitates the human brain’s neural pathways in processing data, using it for decision-making, detecting objects, recognizing speech, and translating languages. It learns without human supervision or intervention, pulling from unstructured and unlabeled data.

Deep learning processes machine learning by using a hierarchical level of artificial neural networks, built like the human brain, with neuron nodes connecting in a web. While traditional machine learning programs work with data analysis linearly, deep learning’s hierarchical function lets machines process data using a nonlinear approach.

Keras, TensorFlow and Pytorch are the three most popular deep learning frameworks. Let’s learn in detail each of these three.

#keras #tensorflow #pytorch #python

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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

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In today’s world, Artificial Intelligence is imbibed in the majority of the business operations and quite easy to deploy because of the advanced deep learning frameworks. These deep learning frameworks provide the high-level programming interface which helps us in designing our deep learning models. Using deep learning frameworks, it reduces the work of developers by providing inbuilt libraries which allows us to build models more quickly and easily.

In this article, we will build the same deep learning framework that will be a convolutional neural network for image classification on the same dataset in Keras, PyTorch and Caffe and we will compare the implementation in all these ways. Finally, we will see how the CNN model built in PyTorch outperforms the peers built-in Keras and Caffe.

- Topics covered in this article
- How to choose Deep learning frameworks.
- Pros and cons of Keras
- Pros and cons of Pytorch
- Pros and cons of Caffe
- Hands-on implementation of the CNN model in Keras, Pytorch & Caffe.

#caffe #deep learning #keras #pytorch #tensorflow