Caffe - Ep. 20 (Deep Learning SIMPLIFIED)

Caffe is a Deep Learning library that is well suited for machine vision and forecasting applications. With Caffe you can build a net with sophisticated configuration options, and you can access premade nets in an online community.

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Caffe is a C++/CUDA library that was developed by Yangqing Jia of Google. The library was initially designed for machine vision tasks, but recent versions support sequences, speech and text, and reinforcement learning. Since it’s built on top of CUDA, Caffe supports the use of GPUs.

Caffe allows the user to configure the hyper-parameters of a deep net. The layer configuration options are robust and sophisticated – individual layers can be set up as vision layers, loss layers, activation layers, and many others. Caffe’s community website allows users to contribute premade deep nets along with other useful resources.

Vectorization is achieved through specialized arrays called “blobs”, which help optimize the computational costs of various operations.

Have you ever used the Caffe library in one of your Deep Net projects? Please comment and share your experiences.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal