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Introduction About CAffe2 in deep learning?

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What is Caffe2?

A New Lightweight, Modular, and Scalable Deep Learning Framework.

Caffe2 aims to provide an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2's cross-platform libraries.

Caffe2 (Convolutional Architecture for Fast Feature Embedding) is an open source, high-performance framework for the development of machine learning models.

Caffe2 is a popular framework due to its speed. The framework can process over 60 million images per day with a single high-performance GPU, like the Nvidia Tesla K40. The framework takes only one millisecond per image for inference and four milliseconds per image for learning.

Caffe2 supports many types of deep learning models and is specialized in image segmentation and image classification. Supported types include convolutional neural networks (CNN), recurrent neural networks (RNN), long short term memory (LSTM) and fully connected neural network designs. 

The framework supports Intel CPU acceleration and Nvidia GPGPU along with multi-graphics card implementations. Caffe2 will support AMD OpenCL, FPGAs, AI accelerators and CNN processors.

Introduction Video for Caffe2

posted Aug 30, 2018 by anonymous

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What is Caffe?

Caffe is a deep learning framework made with expression, speed, and modularity in mind

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  • Modularity: new tasks and settings require flexibility and extension.
  • Openness: scientific and applied progress call for common code, reference models, and reproducibility.
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Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.

Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.

Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convent implementations available.

The Video for Caffe

https://www.youtube.com/watch?v=8KhAqAoQKvg​

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In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach.

Video for Deep Learning

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Video for Linear Regression

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