TensorFlow Dev Summit 2017: Integrating Keras and TensorFlow

TensorFlow Dev Summit 2017: Integrating #Keras and #TensorFlow

  • My choice has fallen to a presentation by François Chollet of the deep learning library API Keras and its integration with TensorFlow.
  • As Dr. Chollet explains, Keras integrated with TensorFlow promises to streamline deep learning frameworks in ways that will be increasingly user-friendly, rendering the mass adoption of these software developments a more feasible reality:

    Dr. François Chollet is the primary author of Keras, developing this tool while at Research at Google.

  • For instance the way video with text data is processed with the Keras-TensorFlow integration is nicely described with the stack of CNNs, LSTMs and dense final layers with softmax being features explained by Dr. Chollet.
  • The best practises advised by Dr. Chollet about the initialization of recurrent weighs of  the neural network is worth to listen, even if the experienced practitioner feels bored.
  • A final note to the confirmation by Dr. Chollet of the capacity of TensorFlow to streamline  a CloudML or a hyperparameter tuning process with just a few lines of code, enabling a distributed training platform able to enhance big data computes with productivity gains.

I am briefly sharing a video from the last TensorFlow Dev Summit in February 2017. My choice has fallen to a presentation by François Chollet of the deep learning library API Keras and its integration with TensorFlow. As Dr. Chollet explains, Keras integrated with TensorFlow promises to streamline deep learning frameworks in ways that will…
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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

Lighting the way to deep machine learning

Lighting the way to deep #MachineLearning #DataScience

  • The data set iterator receives as input a closure that constructs the Torchnet data set object.
  • Torchnet provides a framework on top of a deep learning framework (in this case, torch/nn ) that makes rapid experimentation easier.
  • For instance, small subpackages that wrap vision data sets such as the Imagenet and COCO data sets, speech data sets such as the TIMIT and LibriSpeech data sets, and text data sets such as the One Billion Word Benchmark and WMT-14 data sets.
  • Although machine learning and artificial intelligence have been around for many years, most of their recent advances have been powered by publicly available research data sets and the availability of more powerful computers – specifically ones powered by GPUs.
  • The modular Torchnet design makes it easy to test a series of coding variants focused around the data set, the data loading process, and the model, as well as optimization and performance measures.

Read the full article, click here.


@MikeTamir: “Lighting the way to deep #MachineLearning #DataScience”


Building rapid and clean prototypes for deep machine-learning operations can now take a big step forward with Torchnet, a new software toolkit that fosters rapid and collaborative development of deep learning experiments by the Torch community.


Lighting the way to deep machine learning