Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep Learning | Simplilearn

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This Deep Learning tutorial covers all the essential Deep Learning frameworks that are necessary to build AI models. In this video, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.

Below are the different Deep Learning frameworks we’ll be discussing in this video:
1. TensorFlow (01:28)
2. Keras (02:54)
3. PyTorch (05:02)
4. Theano (06:30)
5. Deep Learning 4 Java (07:55)
6. Caffe (09:51)
7. Chainer (11:29)
8. Microsoft CNTK (13:48)

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Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.

Why Deep Learning?

It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.

And according to, the median salary for engineers with deep learning skills tops $120,000 per year.

You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:

1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence

There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:

1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning

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Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Thanks for watching the video. Cheers!

Abraham Davis says:

Please do one for 2020 thanks

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Doctor Bright says:

So Keras have more to offer and it users Tensoflow because its the most stable neuronal network?

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