Machine Learning

Top 10 Machine Learning Frameworks

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In this article, we will discuss about the Machine Learning and Top 10 Machine Learning Framework in detail.

Machine Learning:

Machine learning could even be defined because the study which provides the system ie., (computer) to search out  automatically on its own experiences it had and improve accordingly without being explicitly programmed.

Ml is an application or subset of AI. The era of machine learning cares with the quality questions for some way to induce computer programs which is able to be automatically improves with their experience.

While we implementing an Machine Learning method requires a many data, which is understood as training data, that’s fetch into the tactic and supported these data, the machine learning for performing a specified tasks.

The info like text, images, audio, etc. It is also referred to as self-learning algorithm. It’s to permit the machines to find out by themselves by their experience with none human intervention or help. According to project requirements, there are various  Machine Learning Frameworks, would be used by Machine Learning developers.

These frameworks allows for the ML developers, it can be used to create models easier, which terms of their specifications for providing an interface, libraries, and organized Machine Learning tools all in one place! In this article, it is revealed that the 10 most popular Machine Learning Frameworks recently used these days.

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TOP 10 MACHINE LEARNING FRAMEWORKS

A Machine Learning Framework, it is an interface, library or tool which can be allowing developers by creating  machine learning models as easier. Let we will discuss the Top 10 Machine Learning Frameworks in detailed:

  • TensorFlow
  • Theano
  • Scikit-learn
  • Caffe
  • Apache Mahout
  • Apache Spark
  • PyTorch
  • Amazon SageMaker
  • Accord.NET
  • Microsoft Cognitive Toolkit

1. TensorFlow

TensorFlow, it is a free end-to-end open-source platform. In the Machine Learning, it  has a wide variety of tools, libraries, and resources. We can create the Machine Learning models with high-level API’s like Keras using TensorFlow. It provides the various levels of abstraction, so we can choose the model which we need. It also allows to deploying the models like cloud, browser, or any device.  It is available in Python and C APIs and also in C++, Java, JavaScript, Go, Swift, etc.

2. Theano

Theano is an open-source project, it is a Python library that allows us to evaluate  the mathematical expressions. It also provides the integration facilities with NumPy by using numpy.

It provides the dynamic C code generation which also evaluates the expressions faster. It means that the operations in which complex mathematical expressions required to be repeatedly calculated could be performed as  much faster by minimized amount of compilation over it.

3. Scikit-learn

Scikit-learn is a free software library for Machine Learning coding in the Python programming language. Scikit-learn is created  on top of other Python libraries like NumPy, SciPy, Matplotlib, Pandas, etc. and so it also provides full interoperability with those libraries.

We can implement several types like Supervised and Unsupervised Machine learning models on Scikit-learn like Classification, Regression, Random Forests, Naive Bayes, Decision Trees, Clustering, etc. with Scikit-learn.

4. Caffe

CAFFE is a deep learning framework written in C++ that has an expression architecture easier allows us to switch between the CPU and GPU. It is abbreviated as CAFFE (Convolutional Architecture for Fast Feature Embedding).

Caffe also had a MATLAB and Python interface and Yahoo has also combined Apache Spark with Caffe to create CaffeOnSpark. It is also the perfect framework for image classification and segmentation as which it can be supported in  various GPU- and CPU-based libraries such as NVIDIA, cuDNN, Intel MKL, etc.

5. Apache Mahout

Apache Mahout is a free Machine Learning framework that is mainly focused on Linear Algebra. It allows data scientists to implement their mathematical algorithms in an interactive environment.

The core algorithms used for clustering, classification, and batch based on collaborative filtering in Apache Mahout used in  Apache Hadoop. It also works for and distributes alongside an interactive shell plus, as a library to link the application.

6. Apache Spark

Apache Spark is an open-source cluster-computing framework, it has provided in the programming interfaces for entire clusters. Spark SQL used in the DataFrames, for provided support for structured and semi-structured data. We can also access data from Multiple sources such as the Hadoop Distributed File System, or non-relational databases such as  Apache Cassandra, Apache HBase, Apache Hive, etc.

7. PyTorch

Pytorch is an open-source Torch library, PyTorch provides TorchScript, which facilitated the seamless transition among the eager mode and graph mode. Like that, the torch distributed backend provided the scalable distributed training for Machine Learning and optimized performance.

8. Amazon SageMaker

Amazon SageMaker is a fully integrated development environment (IDE) for Machine Learning.AWS, provides this Machine Learning service for applications such as Computer Vision, Recommendations, Image, and Video Analysis, Forecasting, Text Analytics, etc.

 The Amazon SageMaker Autopilot also has an automatic machine learning capability that permits you to try to to all this automatically. Amazon SageMaker also allows you to make Machine Learning algorithms from scratch thanks to its connections to TensorFlow and Apache MXNet. You can also connect your ML models to other Amazon Web Services such as AWS Batch for offline batch processing, Amazon DynamoDB database, etc.

9. Accord.NET

          Accord.NET could be a Machine Learning framework that’s completely written in C#. Accord.NET covers on various indices like statistics, machine learning, artificial neural networks with various Machine learning algorithms like Classification, Regression, Clustering along with the help of audio and image processing libraries.

10. Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit is a Machine Learning framework We can easily developing the popular deep learning models like the feed-forward DNNs, convolutional neural networks and recurrent neural networks using the Microsoft Cognitive Toolkit. This toolkit uses multiple GPUs and servers providing parallelization across the backend.We can also using this Toolkit as a customizable manner as  per our requirements with your metrics, networks, and algorithms.

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