Machine learning (ML) according to Wikipedia is a scientific study of statistical models and algorithms to help a computing system to accomplish designated tasks efficiently and independently by relying solely on inferences and patterns extracted from the training or acquired data. It is a sub-set of Artificial Intelligence technology. Thereby, it automatically leans and improves the performance with the pace of time, interactions, and experiences, and most important acquisition of useful data pertaining to the tasks allocated.
Why Machine Learning Framework?
We know in the software industry that without an interface, libraries, and organized tools, software development proves a nightmare. When we combine these essentialities, it becomes a framework or platform for easy, quick, and meaningful software development.
ML frameworks help ML developers to define ML models in precise, transparent, and concise ways. ML frameworks used to provide pre-built and optimize components to help in model building and other tasks.
When we are going to choose the right ML framework for our next project, we must keep the following things in mind.
• The framework should performance-oriented.
• It must provide traditional ways to create models and provide a developer-friendly tool.
• It should quick to grasp and code.
• It ought to out-of-box, not a black-box of a pilot’s cockpit!
• It has to offer parallelization to distribute the computational process.
• It must have awesome user-base and supporting community to help.
Let’s learn about the top ten machine learning frameworks to make the right choice for your next ML application development project. Before dive deeper, I would like to acknowledge you that
• Some ML frameworks are more mathematically oriented.
• Some focus on deep learning while some neural network.
• Some are rich in linear algebra tools while some statistical tools.
I hope these insights help you ahead in the selection.
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It is an excellent open-source library for data-based programming for a wide range of tasks. It supports regressions, classifications, and neural networks like complicated tasks and algorithms. You can run it on CPUs & GPUs both.
TensorFlow has complicated functions and paradigms, so it demands additional efforts while learning it. If you have grasped Numpy arrays of Python framework and its libraries, your job becomes easy while working with an n-dimensional array in TensorFlow framework.
It is a flexible framework and offers multiple models as well as versions, which are serving simultaneously. So, it helps in non-automatic migration to newer versions. The framework can run on the GPU, CPU, servers, desktops, and mobile devices. Therefore, it is portable. It is useful in research and development purposes. It provides auto differentiation and performance.
2. CAFFE (Convolutional Architecture for Fast Feature Embedding)
Caffe 2 is a deep learning tool and highly effective ML framework written in CPP. It supports the major schemes and connected with neural network design. It provides GPU & CPU-based acceleration. Therefore, Caffe 2 is popular in academic research projects and startup prototypes.
It is the fastest framework for deep neural networks and supports GPU training for sample data. It has a well-structured Mat Lab and Python interface to work. It allows switching between CPU & GPU with setting a single flag to train and deploy to commodity clusters.
CAFFE offers great performance and renders it perfect for industrial deployment as well as research experiments. It can process 60M images per day with a single GPU and provides the fastest convent implementation.
3. PyTorch or TORCH
The Torch is an ML library and scientific computing framework. It comes with scripting language interface from Lua programming UI. Torch used by Facebook, IBM, Yandex, and Idiap Research Institute.
Torch is flexible and offers high-end efficiencies and speed. It offers a lot of pre-trained modules. The most distinct thing in Torch is numbers, not an integer, short or double. Thus, it eases various operations & functions.
4. Apache Spark
Spark is a cluster-computing framework mainly written in Java, Scala, R, and Python. It has Spark RDD data structures, so you need not worry about Numpy array. It also lets you work with Spark SQL frames.
It offers simple APIs for data scientists familiar with R & Python. It is scalable and capable of running machine learning code on small to large machines. It is streamlined end-to-end and compatible.
Scikit-Learn is a powerful Python library for machine learning coding and explicitly used in the creation of Models. It has built on other libraries such as SciPy, Numpy, and matplotlib. It is a highly efficient tool for classification, regression, and clustering like statistical modelling.
It comes with supervised & unsupervised learning algorithms as well as cross-validation. It is written in Python, but its core is in Cython for the sake of performance. Cython wrapper around LIBSVM helps in the implementation of support vector machines.
It is highly efficient for data mining and supports various practical tasks. It is capable of dealing with complex tasks with ease and speed.
6. Microsoft CNTK (Cognitive Toolkit)
Microsoft has created it intending to provide a toolkit for deep learning and to use it for a training algorithm to learn like biological brains of humans. However, we can use CNTK to create various machine learning models. For instance, feed-forward DNNs, recurrent neural networks, and convolutional neural networks.
It facilitates neural networks to go through huge and unstructured datasets. It offers rapid training time and intuitive architecture. You can customize it as per your requirements because it lets you select your metrics, networks, and algorithms. It supports multi-machine-multi-GPU-back-ends.
7. Apache Mahout Samsara
It is an exclusive tool for mathematicians, data scientists, and statisticians. It lets its user execute their algorithms quickly. It is an excellent choice for the development of machine learning applications with scalable performance, as it also acts as a distributed linear algebra framework.
It is chiefly focusing on filtering, collaborative grouping, and classification. It lets developers create their mathematical calculation in an interactive environment. The calculation can run on a Big Data platform. Moreover, you can shift the same code in your applications as well as implementations.
It provides a distributed linear algebra and statistic engine, which works and distributes together along with an interactive shell plus a library to link the application. It can climb onto Apache Hadoop with the help of a map or reduce paradigm, but with enough freedom.
8. Apache Singa
It is the best fit for Big Data analytics. It is a flexible and scalable framework for ML because it provides a flexible architecture for scalable distributed training for Big Data. It runs on a wide range of hardware, so it is extensible. NLP and image recognition as main application areas for the framework. It supports traditional ML models, including regression.
9. Amazon Machine Learning
Amazon also has wetted its legs in ML field by providing its machine learning framework. Fundamentally, AML is a collection of tools and wizards to create highly sophisticated, intelligent, and high-end applications without tinkering code.
AML provides predictions for your application using the power of easy to use APIs. To create new models, you have to carry out various operations, including regression, multi-class categorization, and binary classification. In such a scenario, AML can connect your applications to the cloud.
It is an ML framework for scientific computing based on Dot Net technologies. It provides various libraries for different applications such as statistical data processing, image processing, linear algebra, and neural networks. It has libraries, which are available as source code, installer, and NuGet packages. Its matrix library offers reusability and slower algorithmic changes.