An effort undergoing incubation
Singa is known as incubating project to develop an open source deep learning library. The Singa project was initiated by the DB System Group in the year 2014 at Singapore’s National University. It supports different types of deep learning models such as Recurrent Neural Networks (RNN), Restricted Boltzmann Machine (RBM), and Convolutional Neural Networks (CNN) etc. Singa is based on a flexible architecture, it runs several hybrid, asynchronous, and synchronous frameworks for training. Software of Singa has three main components i.e. Model, IO, and Core. The component named Core is concerned with the tensor operations and memory management. IO carries the classes for to read and write the data to disc and the network. Model carries the algorithms and data structures for machine learning models.
Below are some of highlights of Singa-
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Algorithms in Tensor Flow
Before getting into the machine learning algorithm, it will be good for you to expand your knowledge about using the tools correctly. Suppose you are writing Python code without a useful computing library, how it will feel like? It will be like using a smartphone without internet connection. You also install a robust and eminent library named NumPy by installing the TensorFlow library that helps in doing mathematical operation in Python. Machine learning algorithms need great amount of mathematical operations. Initially you need to ensure everything is in right order.