Google, Facebook, IBM, Microsoft and other tech giants including renowned developers have already taken a nimble step towards the Machine learning and Artificial intelligence to make the dream of human beings of creating a highly intelligent machine. And to armed others to partake in this journey of building a conscious machine for the future, there are quite a good number of open source tools avail by tech giants to integrate artificial intelligence into applications.
12 Open-source Machine Learning Tools or Framework 2020
Yet, the artificial intelligence and machine learning are at very early stage, so don’t expect something like some sci-fi movie, however developers those are into AI and ML can use the open-source software we are going to discuss for writing apps for better speech recognition, Image recognization, Voice assistance, developing a neural network and more… This article provides an overview of some of the most popular open-source solutions.
TensorFlow AI development framework is an innovation of Google’s Brain Team that uses an open-source library for machine learning. The developers can use its flexible ecosystem of tools and community resource to develope modern ML-powered applications. It also includes the CUDA Deep Neural Network library.
To build and train models it uses a high-level Keras API and offers multiple levels of abstraction so that one can choose the right one to his project.
Furthermore, for mobile devices, Google in 2017 has released Tensor Flow lite to use the deep learning model on smartphones and in their apps. It becomes increasingly popular, and some devices now even use special accelerator chips for the corresponding application. In order to further improve the use of Tensorflow machine learning library.
For large ML training tasks, the Distribution Strategy API is available for distributed training on different hardware configurations without changing the model definition.
To install it, pip is needed, if you already have pip then here is the command for CPU and GPU version of Tensorflow:
pip install tensorflow
Learn more about TensorFlow installation.
Caffe- Deep Learning Framework
Caffe is a pure C ++ / CUDA architecture open-source project that supports command line, Python, and MATLAB interfaces; you can switch directly between the CPU and GPU. Models and corresponding optimizations are given in text rather than code. Caffe gives the definition of the model, optimization settings, and pre-trained weights, so it’s easy to get started immediately.
Caffe is used in combination with cuDNN to test the AlexNet model. It only takes 1.17ms to process each picture on the K40. You can use the various layer types provided by Caffe to define your own model.
Caffe can be used, for example, for speech recognition, the recognition and classification of images or for the development of natural languages in AI devices.
See this project Github page.
You can install on Ubuntu for CPU
sudo apt install caffe-cpu and for GPU
sudo apt install caffe-cuda
PyTorch is another open-source machine learning framework. It includes TorchScript, a compiler that converts PyTorch models into a statically typed graph. Data scientists can then carry out optimizations and adjustments in eager mode and thereby incrementally convert their models to TorchScript. It can be used to implement network architectures like RNN, CNN, LSTM, etc and other high-level algorithms.
PyTorch has known the Open Neural Network eXchange (ONNX) format for exporting trained or untrained models to a standardized format for some time. ONNX enables the exchange between different frameworks and, for example, simplifies the transfer of models from PyTorch to TensorFlow and vice versa.
In addition, the connection to TensorBoard it includes Python-based libraries based on Toch such as torchaudio, torchtext, torchvision and more.
Microsoft CNTK- Opensource machine learning toolkit
Microsoft’s Computational Network Toolkit (CNTK) was originally developed to make faster progress in the field of speech recognition. The deep learning toolkit was therefore used internally to improve the speech recognition of the digital assistant Cortana. Microsoft later released the software on GitHub under an open-source license. Thus, anybody now can try his hands on it. CNTK is also one of the first deep-learning toolkits to support the Open Neural Network Exchange ONNX format.
The Microsoft Cognitive Toolkit represents neural networks as a series of computational steps via a directed graph with the help Network Description Language (NDL).
It is flexible and supports C++, C#, Java, and Python.
scikit-learn – machine learning with Python
The scikit-learn is another Python library based machine learning framework that derived from SciPy Toolkit. It uses packages like NumPy, SciPy, or Matplotlib to write mathematical, scientific or statistical programs in Python. It can be used for data mining and data analysis.
It is also open-source and available for commercial purposes. We can use it for building applications for various categories such as classification purposes to identifying which category an object belongs to; in predicting a continuous-valued attribute associated with an object; an automatic grouping of similar objects into sets; comparing, validating and choosing parameters and models; Transforming input data such as text for use with machine learning algorithms, AI bots and more…
If you want to work with machine learning and artificial intelligence-based on Python, you should take a look at the possibilities of scikit learning. The providers also provide various tutorials that developers can use to work with Python and scikit-learn.
Apache Mahout – Machine Learning for Big Data
Mahout is an open-source project under the Apache Software Foundation (ASF) based on Apache Hadoop and MapReduce. It provides some scalable implementations of classic algorithms in the field of machine learning to help developers in creating smart applications more easily and quickly. Mahout includes many implementations, including clustering, classification, filtering, and data mining. In addition, Mahout can be effectively extended to the cloud by using the Apache Hadoop library.
Apache Spark is a fast and general-purpose computing engine designed for large-scale data processing. Spark is a Hadoop MapReduce-like general-purpose parallel framework open-sourced by UC Berkeley AMP lab (UC Berkeley’s AMP Lab). Spark has the advantages of Hadoop MapReduce; but unlike MapReduce, the intermediate output of the job can be stored in memory, so it is no longer necessary to read and write HDFS, thus Spark can be more suitable for data mining and machine learning algorithms that need iterative MapReduce algorithms.
MLlib (Machine Learning lib) is Spark’s implementation library for commonly used machine learning algorithms. It also includes related tests and data generators. MLlib currently supports 4 common machine learning problems: classification, regression, clustering, and collaborative filtering.
Spark MLlib can be used with Java, Scala, Python and R. MLlib uses Spark’s APIs and interacts with NumPy in Python. NumPy is a Python library that can be used to handle vectors, matrices and multidimensional arrays…
Keras: The Python Deep Learning library
Keras is a high-level neural networks API, written in Python, capable of running on top of TensorFlow, CNTK, Theano, or MXNet. Since its initial release in March 2015, it has gained favour for its ease of use and syntactic simplicity, facilitating fast development. It’s supported by Google.
It is not like other end-to-end machine learning framework and works as an interface. It uses a high-level of abstraction, thus, easy to handle neural network configuration without giving much heed on what kind of framework it is working on.
Theano is based on Python and is a library that is good at processing multi-dimensional arrays (similar to NumPy in this respect). It was designed to perform the operations of large-scale neural network algorithms in deep learning. Theano’s early developers were Yoshua Bengio and Ian Goodfellow. Due to their academic background, it was originally designed for academic research. When combined with other deep learning libraries, it will be very suitable for data exploration.
Theano can be better understood as a compiler for mathematical expressions: define your desired result in a symbolic language, and the framework will compile your program to run efficiently on the GPU or CPU.
For a long time in the past, Theano has been the industry standard for deep learning development and research. Compared to a deep learning library, it is more like a research platform. You need to do a lot of work from the bottom to create the models you need, which means that it is very flexible.
No matter how good the tools are, they have withdrawn from the stage of history. Theano, which once won the favour of the academic community, is no exception.
However, the key developers of this deep learning framework have stopped their contribution in 2017, yet its community is still actively supporting the project which can be seen clearly on the Github.
Oryx- Deep learning framework
Oryx is also a framework that uses data from Kafka and Spark to help Hadoop users to build models for real-time large scale machine learning. Data can be processed in real-time from different sources.
It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering.
Accord.NET Machine Learning Framework
The Accord.NET machine learning framework based on .NET which provides a wide range of possibilities, it can be used for machine learning, statistics, audio and signal, Kernel methods, hypothesis tests, Artificial intelligence, clustering, computer vision and image processing for commercial use on Microsoft Windows, Xamarin, Unity3D, Linux or mobile.
It can prove a good choice to handle artificial neural networks numerical optimization and even visualization.
MLPack: C++ machine learning library
mlpack is for those looking for a great flexible, memory friendly machine learning platform written in C++. It is fast with Python bindings and C++ classes which can then be integrated into larger-scale machine learning solutions. Well, in terms of the documentation, the user could struggle.
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