- Caffe2 is a deep learning framework enabling simple and flexible deep learning.
- Built on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind, allowing for a more flexible way to organize computation.
- Caffe2 aims to provide an easy and straightforward way for you to experiment with deep learning by leveraging community contributions of new models and algorithms.
- Caffe2 comes with native Python and C++ APIs that work interchangeably so you can prototype quickly now, and easily optimize later.
- Caffe2 is accelerated with the latest NVIDIA Pascal™ GPUs and scales across multiple GPUs within a single node.
Run deep learning training with Caffe2 up to 3x faster on the latest NVIDIA Pascal GPUs.
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- Google Machine Learning Models for Image Captioning Ported to TensorFlow and Open-Sourced
- The model comes up with a caption that hadn’t previously existed.
- The model appears to address this problem by introducing a fine-tuning phase that allows the model to extract information useful for describing details of objects, exclusive of the classification phase.
- Google chronicled their journey over the past few years with their announcement around open-sourcing a TensorFlow model for image captioning, and some of the testing for comparing accuracy and performance benchmarks between the new approach and existing implementations.
- It splits the image classification phase for identifying objects from another phase that adds adjectives and prepositional phrases, and from a phase in which the model gives the caption structure to make it more syntactically correct and humanlike.
As TensorFlow becomes more widely adopted in the machine learning and data science domains, existing machine learning models and engines are being ported from existing frameworks to TensorFlow for improved performance, furthering the adoption and success of the open-sourced project.
Continue reading “Google Machine Learning Models for Image Captioning Ported to TensorFlow and Open-Sourced”
- 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
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@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