There has been exponential growth in the field of Machine Learning/Artificial Intelligence and it has created a boom in the technology field. According to the reports, it has created millions of jobs and has led to the evolution of many Machine Learning frameworks. Today we will look into some of these most popularly used Machine Learning Frameworks.
What is Machine Learning?
Machine Learning is a part of Artificial Intelligence, consisting of algorithms that work on outputs generated from previous experiences. The more input parameters (more experiences) they get, the better the results can be expected. The uniqueness of these algorithms is that they don’t require human intervention. But, the human brain is always required to understand which machine learning algorithm will fit best for a particular situation.
Data sets (inputs) are divided into training and testing sets. Machine learning algorithms work on the training data sets in order to build models for prediction and decision making. Some applications of machine learning are computer vision, collaborative filtering, natural language processing, spam filtering, etc.
What is a Machine Learning Framework?
ML models can be easily developed with the help of Machine Learning frameworks, without knowing the ML Algorithm.
Python is the most widely used language in machine learning and so most of the ML frameworks are built for programming in Python language.
Machine Learning with R Programming is also widely used in Data Science fields.
Read More:- IS PYTHON ENOUGH FOR MACHINE LEARNING
10 Machine Learning Frameworks widely used are:-
- Amazon SageMaker
- Spark ML
- Microsoft Cognitive Toolkit (CNTK)
Google’s Tensorflow is the most popularly used framework for machine learning/deep learning. It is an open-source platform for machine learning. ML Applications can be easily built and deployed using Tensorflow. It contains a broad range of multiple libraries, tools and community resources that greatly enriches the developers' experience and makes the development easy.
Keras is a neural network library built on top of TensorFlow to make Machine Learning modeling easier. It simplifies some of the coding steps, like offering all-in-one models, and can use the same Keras code to run on a CPU or a GPU.
Keras is a tool designed for human beings, not machines. Its features and working like load balancing, steady and easy APIs, minimizing the user inputs to execute use cases and providing simple, clear and actionable error messages greatly benefits the developer's coding experience.
Scikit-Learn is the most popular and frequently in-use ML library. It features various algorithms that are designed to work efficiently with the Numpy and Scipy. These algorithms comprise regression, classification and clustering including k-means, random forests, gradient boosting, DBSCAN, SVM (Support Vector Machine).
Theano is a Python library built on top of Numpy Library. It is primarily used to evaluate multi-dimensional arrays and expressions that require mathematical manipulation.
Mathematical expressions are compiled to execute properly on CPU/GPU architectures and are presented in a Numpy-Esque syntax.
Amazon SageMaker was released on 29 November 2017 that provides an integrated development environment for machine learning models. AWS provides this Machine Learning service for applications such as Computer Vision, Collaborative Filtering, Image and Video Analytics, Forecasting, Text Analytics, etc. You can choose Amazon SageMaker to build, train, and deploy machine learning models on the cloud.
All this can be automated using Amazon SageMaker Autopilot which has capabilities to automate machine learning models. Amazon SageMaker allows you to create ML algorithms from scratch as it is connected to Tensorflow and Apache MXNet.
Spark ML Lib
Apache Spark provides an interface to programmers for complete clusters. It is a widely popularly used open-source cluster-computing framework. Spark Core is the base of the Apache Spark. It provides in-memory computation to increase the speed and also allows the parallel processing of big data. Spark SQL works more efficiently and easily to optimize the structured data set. It is the distributed framework that works on structured data processing. Spark Streaming is one of the widely used live streaming & high scalable processing that ensures fault-tolerant solutions. It works on dividing the live dataset into multiple small batches before processing.
Spark MLib is Spark's machine learning library which has very advanced algorithms, highly scalable and high-speed functionalities. It consists of algorithms like clustering, regression, classification, dimensionality reduction, collaborative filtering.
Microsoft Cognitive Toolkit (CNTK)
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit developed by Microsoft Research. With the help of CNTK, users can easily combine models like recurrent neural networks (RNNs), convolutional neural networks (CNNs) and DNNs. It has explained neural networks as a series of computational steps. Automatic parallelization and differentiation across servers and GPUs can be implemented with the help of stochastic gradient descent (SGD, error backpropagation) learning. Multiple GPUs and servers are used to provide parallelization across the backend.
H20 is a decision-making artificial intelligence tool that provides business-oriented insights to the users. It is an open-source machine learning platform used for fraud analytics, healthcare, risk analytics, modeling, insurance analytics, financial analytics, and customer intelligence.
Caffe is provided by the Berkeley Vision and Learning Center (BVLC) and by network donors. Caffe Framework is used by Google’s DeepDream. This popular learning structure is a BSD-authorized C++ library with Python Interface. It is made with the best quality and high speed.
Torch framework provides support for ML algorithms to GPUs first. It is built on an easy and fast scripting language LuaJIT and an underlying C/CUDA implementation, which makes it easy to use and efficient.
The goal of this framework is to have maximum flexibility and speed in building your ML algorithms.
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