Top 10 Machine Learning Softwares among Learners and Professionals
In this article, we will be looking at the top softwares for Machine Learning.
Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
Machine learning algorithms use historical data as input to predict new output values. These softwares are great for running your ML code. There are countless number of softwares and tools in the industry.
We will be looking at popular ones among both learners and professionals. It will provide you all the information about Machine Learning softwares and tools.
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Machine Learning Softwares
These are the top 10 Machine Learning Softwares:
- Apache Mahout
- Apache Singa
- Amazon Machine Learning (AML)
- Google Cloud ML Engine
Tensorflow is a free and open-source tool for Machine Learning. It is a cloud-based platform that allows users to create and run ML algorithms or models. It is often used across a variety of tasks but features a particular specialize in training and inference of deep neural networks.
Tensorflow may be a symbolic math library supported dataflow and differentiable programming.
Basically, Tensorflow is a product of Google. It is also a computational framework that helps in building large scale ML models. It uses python for front-end APIs for creating applications in the framework. These applications are executed in high-level C++. It is used in image recognition, handwriting classification, recurrent neural networks, etc.
Tensorflow can run smoothly on both CPU and GPU. It provides good libraries to prevent long coding.
2. Apache Mahout
Apache Mahout, a project of the Apache Software Foundation to supply free implementations of distributed or otherwise scalable machine learning algorithms focused totally on algebra.
It may be a distributed algebra framework and mathematically expressive Scala DSL. It is designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms.
The mahout is a data mining framework that uses Hadoop in its background. It can process and manage a huge amount of data using Hadoop.
The mahout is a framework of apache. It includes mainly matrix and vector libraries which help in performing complex calculations.
Performs deep learning computations by providing an extensible Scala DSL and also provides a distributed linear algebra framework.
There are various famous companies that are using apache mahout. The user interest selection in Twitter uses mahout. It is one of the most used Machine Learning software in ML projects worldwide. Apache Mahout turns big data into useful information. It is a fast and efficient way to increase your business capabilities.
3. Apache Singa
Apache SINGA is an Apache top-level project for developing an open source machine learning library. It provides a versatile architecture for scalable distributed training, is extensible to run over a good range of hardware, and features a specialize in health-care applications.
Apache Singa as the name suggests was developed in Singapore’s NUS. The Apache Singa is a ML library and a project of Apache. This was created to train large scale ML models over a cluster of machines. This Machine Learning software is widely used for NLP and image recognition. It provides device abstraction while running on hardware devices. It provides a very flexible architecture for training models.
There are various side projects like Singa-lite and Singa-easy. Singa-lite will implement deep learning on 5G devices. Singa-easy is to make AI easier for domain experts with weak AI knowledge. It includes special tools in it. They can perform read, write, encode and decode operations on data and files.
It comprises of three components:
4. Amazon Machine Learning (AML)
Amazon Machine Learning is known to be a robust, cloud-based service that creates it easy for developers of all skill levels to use machine learning technology.
This section introduces the key concepts and terms which will assist you understand what you would like to try to to to make powerful machine learning models with Amazon ML.
AML is a cloud-based platform of Amazon. It provides various wizards and visualization tools.
Amazon Machine Learning is widely used in predictions. It allows users to create and use data from MySQL, Amazon Redshift, etc.
Amazon SageMaker is a service provided by Amazon. As well as, Amazon also provides data security and storage.
Amazon Glacier S3 provides storage and great durability. And Amazon Redshift is used to provide very fast analytics.
Amazon ML services also provide learning tools. Two of them are the DeepRacer and DeepLens. DeepRacer helps to practically learn reinforcement learning.
DeepLens is a video camera used in learning Deep Learning. It is used to create, train and deploy ML models at any scale.
AML generally supports three models:
- Multi-class Classification
- Binary Classification
Accord. Net, as the name suggests, is a .NET machine learning framework. The framework comprises a set of libraries that are available in source code as well as via executable installers and NuGet packages. It is an extension of AForge.NET. It offers libraries on image and audio processing which are written in C# language.
Accord.Net can be used for image stitching, panoramic picture creation, etc. It can blend two pictures by feature extraction. This Machine Learning software requires a skilled workforce to work on it. It can be run on windows, xamarin, Unity3D, etc.
Shogun is a great platform that offers great libraries, algorithms for ML problems. It’s written in the C++ language.
Shogun is a Machine Learning software that provides interfaces in R, Python, JAVA, Ruby, etc. It is not very popular among professionals. But it is among the students. It offers APIs for algorithms that are easier to manage.
This Machine Learning software also helps in the linking of other libraries like LibLinear, SVMLight, etc. Its main objectives are regression and classification.
Shogun is capable of processing a huge amount of data.
7. Google Cloud ML Engine
This platform helps to work with complex algorithms and large data. Google provides a cloud-based platform for ML app developers and data scientists to train and run their models. It is usually used by companies and enterprises for faster response to client emails.
This Machine Learning software helps in training complex models. You can also use the GCP console. It provides suitable user interfaces for your ML projects.
Google Cloud ML Engine supports almost all the tools used in Deep Learning and ML. Hence, it is very helpful for both students and professionals.
Pytorch is a Facebook developed platform. It provides a great framework for Deep Learning and neural networks. It is very useful for building, testing and running your own research prototype.
PyTorch also promotes distributed training. It means you can do parallel work. It can access more than one GPUs at a time. This allows it to take large input in less time.
There are various examples of PyTorch. The most famous one being Uber’s probabilistic programming language. It is entirely built on PyTorch.
Other examples are time sequence predictor, image classifier.
The best part is it also provides dynamic computational graphs. This means that it will tell you how much memory is required for the neural network model. It provides libraries that can be used in codes.
PyTorch can perform math operations like NumPy (which is another library).
Keras is an open-source neural network library. It is written in python and it can run on top of other high-level softwares like TensorFlow, CNTK, and Theano.
This Machine Learning software helps in the fast experimentation of various models and algorithms. It also provides support for CNN (Convolutional Neural Network) and recurrent networks.
Keras models are mainly based on a sequential model and functional APIs. It is believed to be the future of making neural networks. It allows you to run the same code on different back-ends. This is the reason why Keras is so loved.
Keras is an API designed for humans. It learns from the user experience. It handles all the low-level APIs like computational graphs, tensors, etc in the back-end. The high-level API handles the way we make models, it defines layers, it sets up various I/O models.
The core job of Keras is to make things easy while giving the user complete control over it.
H2O.ai is a company whose aim is to make ML easier for everyone. They offer various ML products like H2O, sparkling water, Deepwater, steam and driverless AI.
H2O allows users to switch between Python, R, and other tools. This allows to use the best tool for the project. Ir also provides a front-end tool called flow.
Flow helps you to play with datasets and work on them.
H2O has platforms and versions like H2O-3, H2O4GPU, etc. These provide support to convolutional neural networks and recurrent networks.
From this TechVidvan’s Machine Learning Softwares article, we learned about the various softwares, platforms, and libraries. We also learned about softwares that supports various models.
We also looked at software that helped in modelling and rapid prototyping.