10 Best Development Tools for Machine Learning

machine learning tools

Over the years AI has developed from a theoretical concept to an acceptable technological term that is used in all fields. From self-driven cars, complex medical procedures, welfare, and many other areas. Artificial intelligence was viewed by many as a complex undertaking and was considered as an area for computer geniuses and nerds. However, in recent years learning machines have been developed and are used by industry players to undertake complex tasks with fewer hindrances. In this article, we will highlight some outstanding big data and machine learning tools, and they include:


TensorFlow is an open source framework developed by Google Brain Team. TensorFlow is mainly used for data-programming, and it provides an environment for making algorithms for solving problems such as natural language processing, forecasting, voice recognition, among others. TensorFlow has also contributed to the development of learning applications such as neural networks.

TensorFlow being a mathematical library uses data flow graphs, and it has a central unit of data called tensor consisting of primitive values shaped into an array of several dimensions. It is flexible and can be used in multiple models and makes, thus helping in non-automatic transition to the latest versions. Also, it is highly portable and can use a single API to run on multiple computing platforms such as GPUs, CPUs, desktops, mobile, and servers. Companies such as Twitter, Airbus, and IBM use it for research and production purposes.


Torch is an open source library based on the Lua language and uses strong N-dimensional allay that helps in complex operations such as indexing, transposing and slicing. Torch also offers a linear algebra routine, neural networks, and energy-based models.

PyTorch is used by the Facebook’s AI research group and Uber’s Pyro platform for probabilistic programming is based upon it. Recently, it has adapted its use for IOS and Android systems. PyTorch has a tensor computation that has a powerful GPU acceleration and a deep neural network that is based on an automatic differentiation. It is built on Python control low, and its algorithms have become easier to learn especially to the python programmers. PyTorch is a flexible, efficient, has high speed, and contains numerals pre-trained models that are easily accessible. However, PYTorch lacks clear documentation and has no plug and play capabilities.  

Microsoft CNTK

Microsoft Cognitive Toolkit is an open source deep learning framework developed by Microsoft Research. It supports multiple languages including C++, Java, Python, and C#. It also supports CUDA API system for Nvidia, auto diff systems, and parallel execution as well as numerals pre-trained models.

Microsoft CNTK uses a direct graph for modularization and maintaining a series of computations that a neural network can provide. It has the capability of supporting RNN and CNN models of neural networks, and it is capable of solving problems such as voice, handwriting and images recognition. It also allows for distributed training and can support different model types on several servers. Presently, Microsoft CNTK lacks visualization and does not support ARM-based CPUs making it unstable to run on mobile setups.


Caffe (Convolutional Architecture for Fast Feature Embedding) is a powerful deep learning framework developed by UC Berkeley. It is a high speed, and efficient tool is written in CPP and is primarily used for image classification and segmentation. Like TensorFlow, Caffe connects and support various neural networks models and works well on GPUs and CPUs. Caffe is used for academic research projects as well as developing prototypes.


Apache Spark is a scalable learning library initially developed at Berkeley’s lab and later donated Apache Software Foundation.  It is written in Java, Python, Scala and R programming languages. Apache Spark is a powerful and very fast framework, and it can process large-scale data, and it uses the Numpy arrays and RDD data structures that are easier to understand for data scientists. Due to its scalability, you can run the same ML code on small and big computers. Apache Spark learning algorithms include classification, clustering as well as regression.


Scikit-Learn is a robust and efficient Python framework built from other libraries such as a NumPy, SciPy, as well as matplotlib and it is used in building models. It is also an efficient framework for statistical modeling techniques which include regression, clustering, and classification.

Scikit- learn include features such as the supervised and unsupervised learning algorithms as well as cross-validation. Scikit-learn is an efficient library for data mining, and it is used for complex and practical tasks.


Chainer is a learning framework that is based on Python NumPy and CuPy libraries, and it only supports Python-based interfaces. Chainer earlier ability to adopt in the Define-by-Run ML technique as well as its outstanding performance on a variety of systems has made it popular among digital industry companies.

Chainer provides developers with the ability to modify neural networks when programming thus allowing them to execute the flow statements with little difficulty. It also runs on Nvidia’s CUDA API which enables it to run on several GPU systems. Chainer main uses include speech recognition, machine translation, and sentiment analysis.


Accord.NET is a C# based machine learning framework that is used for easily making audio and image processing. It contains algorithms that perform vision processing and usually used for tasks like facial detection, image integration and tracking moving objects.

Accord.NET can handle numerical optimization, visualization, and artificial neural networks thus maintaining a high-quality display. Other benefits of Accord.Net is that it is well documented, and it has vast and active development partners. However, Accord.NET is not popular and slower when compared with TensorFlow.


Keras is a python deep machine learning library that runs on neural networks API. It is mainly suitable for beginners for it provides an easy way of expressing neural networks in comparison with the other frameworks. It is developed using Python, and it has the capability of running on top of other significant libraries that support neural networks such as CNTK, Theano, and TensorFlow.

Keras is based on four major principles including modularity, extensibility, friendliness, as well as working with Python and it is slower than other libraries.



Theano is a mature and powerful Python library that provide developers with tools to define, optimize and evaluate mathematical expressions involving multi-dimensional arrays with ease and efficiency. Theano consists of the following main features, and they include integration with Numpy, transparent use of GPU for performing intensive data computations and units for testing and self-verification among other libraries. Theano has several shortcomings which include the need to be embedded with other frameworks to gain high abstraction quality, increases the learning curve and its buggy on AWS.

Summing it up

All of the above frameworks are considered among the best learning libraries and are widely used in artificial intelligence as well as other digital fields. Big technology companies such as Google, IBM, Facebooks, Microsoft, Amazon, Yahoo, Apple, and others are using these libraries to develop and analyze deep and powerful machine learning projects. This does not mean that the libraries are only used by big companies but can also be used by ordinary people who have an interest in the field. In whatever level you are in the AI industry, you can use an option from the article.

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