Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people.
Artificial intelligence in the last decade has spurred a huge demand for AI and ML skills in today’s job market. ML-based technology is now used in almost every industry vertical from finance to healthcare. In this blog, we have compiled a list of best frameworks and libraries that you can use to build machine learning models.
Here list of top 10 Open - Source framework for ML and AI
Tensorflow
Theano
Torch
Caffe
Microsoft CNTK
Keras
SciKit - learn
Accord .NET
Azure ML Studio
Amazon machine learning
Tensorflow
"TensorFlow is an open source software library for numerical computation using data flow graphs".
TensorFlow has 3 main components
TensorFlow(API) : This component of tensorflow contains the API's to define the models and train the models with the data
TensorBoard : TensorBoard. It helps to analyze, visualize, and debug TensorFlow graphs.
TensorFlow Serving : This component of tensorflow helps to deploy the pretrained models.Tensorflow serving is capable of switching from old models to new models with out any downtime
Theano
Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy.
Theano features :
Tight integration with NumPy: a similar interface to NumPy’s. numpy.ndarrays are also used internally in Theano-compiled functions.
Transparent use of a GPU: perform data-intensive computations up to 140x faster than on a CPU (support for float32 only).
Efficient symbolic differentiation: Theano can compute derivatives for functions of one or many inputs.
Speed and stability optimizations: avoid nasty bugs when computing expressions such as log(1 + exp(x)) for large values of x.
Dynamic C code generation: evaluate expressions faster.
Extensive unit-testing and self-verification: includes tools for detecting and diagnosing bugs and/or potential problems.
Fore more details about it visit here
Torch
PyTorch is a python package that provides two high-level features: -
Tensor computation (like numpy) with strong GPU acceleration - Deep Neural Networks built on a tape-based autograd system
You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.
Fore more details about it visit here
Caffe
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
Why Caffe?
Expressive architecture
Extensible code
Speed
Community
Fore more details about it visit here
Microsoft CNTK
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Fore more details about it visit here
Keras
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, 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.
Use Keras if you need a deep learning library that:
Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).Supports both convolutional networks and recurrent networks, as well as combinations of the two.Runs seamlessly on CPU and GPU.
Fore more details about it visit here
SciKit - learn
Scikit-learn is probably the most useful library for machine learning in Python. It is on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Fore more details about Scikit-learn visit here
Accord .NET
The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive documentation and wiki helps fill in the details.
Fore more details about Accord.NET visit here
Azure ML Studio
Azure Machine Learning Studio provides the platform for each step from data pre-processing, through the selection of the best model to the deployment of that model to the applications that consume it.
Machine Learning on Microsoft Azure
Azure offers two Machine Learning solutions with different capabilities and advantages:
Machine Learning Studio (for building ML solutions using a collaborative, drag and drop interface and pre-built models – ideal for those who’re new to ML)
Machine Learning Service (a more open platform for creating ML solutions using Python and other open source tooling – ideal for those who are experienced building ML solutions and want to take advantage of public-cloud scalability)
Amazon machine learning
AWS has the broadest and deepest set of machine learning and AI services for your business.
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