1. Keras
Keras is open source is open source library that is particularly focused on experimentation with deep natural network.
Keras is a minimalist Python library for deep learning that can run on top of Theano or TensorFlow.
It was developed to make implementing deep learning models as fast and easy as possible for research and development.
It runs on Python 2.7 or 3.5 and can seamlessly execute on GPUs and CPUs given the underlying frameworks. It is released under the permissive MIT license.
Keras was developed and maintained by François Chollet, a Google engineer using four guiding principles:
Modularity: A model can be understood as a sequence or a graph alone. All the concerns of a deep learning model are discrete components that can be combined in arbitrary ways.
Minimalism: The library provides just enough to achieve an outcome, no frills and maximizing readability.
Extensibility: New components are intentionally easy to add and use within the framework, intended for researchers to trial and explore new ideas.
Python: No separate model files with custom file formats. Everything is native Python.
2. Tensorflow
TensorFlow is free software library that is used for many machine learning like natural network.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
3. Scikit-learn
Scikit-learn is a free software library for machine learning that various classification, regression and clustering algorithms related to this, Also Scikit-learn can be used in conjugation with Numpy and Scipy.
Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.
Simple and efficient tools for predictive data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license
Comments