Keras: Deep Learning library for Theano and TensorFlow

Keras: Deep Learning library for Theano & TensorFlow  #DataScience #bigdata #machinelearning

  • In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.
  • The core data structure of Keras is a model, a way to organize layers.
  • A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).
  • You can now iterate on your training data in batches:

    Alternatively, you can feed batches to your model manually:

    Evaluate your performance in one line:

    Or generate predictions on new data:

    Building a question answering system, an image classification model, a Neural Turing Machine, or any other model is just as fast.

  • For a more in-depth tutorial about Keras, you can check out:

    In the examples folder of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.

Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was 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.
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Deep Learning Frameworks

New #cuDNN 5.1, 2.7x faster training of #deeplearning networks with 3x3 convolutions.

  • Deep learning course: Getting Started with the Caffe Framework
  • Choose a deep learning framework from the list below, download the supported version of cuDNN and follow the instructions on the framework page to get started.
  • Chainer is a deep learning framework that’s designed on the principle of define-by-run.
  • Caffe is a deep learning framework made with expression, speed, and modularity in mind.

Read the full article, click here.


@GPUComputing: “New #cuDNN 5.1, 2.7x faster training of #deeplearning networks with 3×3 convolutions.”


The NVIDIA Deep Learning SDK accelerates widely-used deep learning frameworks such as Caffe, CNTK, TensorFlow, Theano and Torch as well as many other deep learning applications. Choose a deep learning framework from the list below, download the supported version of cuDNN and follow the instructions on the framework page to get started.


Deep Learning Frameworks

Keras: Deep Learning library for Theano and TensorFlow

Keras:Deep Learning library for Theano & TensorFlow Tutorial   #DataScience #MachineLearning

  • The core data structure of Keras is a model , a way to organize layers.
  • By default, Keras will use Theano as its tensor manipulation library.
  • The main type of model is the Sequential model, a linear stack of layers.
  • To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
  • Getting started: 30 seconds to Keras

Read the full article, click here.


@gcosma1: “Keras:Deep Learning library for Theano & TensorFlow Tutorial #DataScience #MachineLearning”


Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was 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: Deep Learning library for Theano and TensorFlow

On the importance of democratizing Artificial Intelligence

On the importance of democratizing #ArtificialIntelligence, from Keras #DeepLearning blog

  • And you, as an early adopter of deep learning, you have a responsibility to make sure that the opportunities that AI will create are open to everyone.
  • The purpose of Keras is to make deep learning accessible to anyone with an idea and with some basic computer science literacy.
  • There is a lot of hype around deep learning right now, and people sometimes have unrealistic short-term expectations.
  • AI will take time to get deployed to its true potential, but when it does, it will have a long-term social and economic impact that most people seem to underestimate.
  • Making deep learning more accessible should be one of our priorities.

Read the full article, click here.


@kdnuggets: “On the importance of democratizing #ArtificialIntelligence, from Keras #DeepLearning blog”


Keras is a Deep Learning library for Python, that is simple, modular, and extensible.


On the importance of democratizing Artificial Intelligence