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Neural Networks Tutorial with Keras and TensorFlow in R Studio

Neural Networks (ANN) in R studio using Keras & TensorFlow. Learn Artificial Neural Networks (ANN) in R. Build predictive deep learning models using Keras and Tensorflow| R Studio

Neural Networks Tutorial with Keras and TensorFlow in R Studio

R interface to Keras

Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras has the following key features:

  1. Allows the same code to run on CPU or on GPU, seamlessly.
  2. User-friendly API which makes it easy to quickly prototype deep learning models.
  3. Built-in support for convolutional networks (for computer vision), recurrent networks (for sequence processing), and any combination of both.

Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine.

Is capable of running on top of multiple back-ends including TensorFlow, CNTK, or Theano.

For additional details on why you might consider using Keras for your deep learning projects, see the Why Use Keras? article.

This website provides documentation for the R interface to Keras. See the main Keras website at https://keras.io for additional information on the project

Neural Networks (ANN) in R studio using Keras & TensorFlow

Learn Artificial Neural Network using Keras and TensorFlow in R. This is a complete online tutorial to master Neural Network models in R Studio.

You've found the right Neural Networks course!

After completing this course you will be able to:

  • Identify the business problem which can be solved using Neural network Models.
  • Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.
  • Create Neural network models in R using Keras and Tensorflow libraries and analyze their results.
  • Confidently practice, discuss and understand Deep Learning concepts


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