Machine learning is an application of Artificial Intelligence where mathematical models of data are used to help computers learn without training. AI and machine learning are closely connected, but they are not alike. Learn more about the future of artificial intelligence in various industries.
In machine learning large volumes of data are fed to a computer algorithm, then learns it, analyses it to find patterns, and data-driven decisions and recommendations are generated.
Machine learning (ML) frameworks are interfaces that permit data scientists and developers to build and use machine learning models or applications more quicker and easier.
Nowadays, almost every industry uses a machine learning framework to improve workflow and decision-making. Finance, insurance, cybersecurity, healthcare, transportation, and the sales and marketing industries are taking advantage of machine learning and artificial intelligence to transform their processes and products.
Let’s, take a look at the most popular machine learning framework that is used today by developers and data scientists.
The TensorFlow machine learning framework is a Google product created by engineers and researchers on the Google brain team. It is versatile and one of the best machine-learning frameworks.
It consists of a wide range of features and an extensive library for programming, neural networks, classification, and regression models.
-Open source and has extensive APIs
-Runs on CPUs and GPUs
-Extremely popular and has lots of community support
-Can be used via script tags or via installation through npm
Scikit-learn machine learning comes with an extensive library for the Python programming language.
It is considered the best machine learning framework, which supports development work in Python and supports designing algorithms and models for classifications, clustering, pre-processing, regression, dimensional reduction, and model selection.
This framework of machine learning is capable of extracting text and images and can also test the accuracy of models on fresh unseen data.
-Works well with Python
-The top framework for data mining and data analysis
-Permits developers to change any pre-set parameters for algorithms.
-Suits well for unsupervised and administrative calculations.
-Open-source and free
CNTK machine learning framework, owned by Microsoft, is used to define neural networks as a series of directed graph-based computational development processes. Microsoft Cognitive Toolkit (CNTK) is a deep learning toolkit.
It permits developers to combine and view various machine learning model types, which comprise recurrent networks, conventional neural networks, and feed-forward deep neural networks.
CNTK machine learning framework is an ultimate choice for large-scale, multi-dimensional, or sparse data sets in C++, Python, and BrainScript.
-Written in C++
-Supports Python and BrainScript
-Work with multiple servers at once to make the learning process faster.
-To train machine models effectively, it provides distributed training.
H2O machine learning framework helps organizations with their decision support system procedures.
Analytics for risk and fraud propensity, insurance customer analytics, patient analytics in healthcare, advertising spending, and ROI, and customer intelligence are all common uses for H2O.
H2O has gone through three revisions, the current version is known as H2O-3. The current version of H2O machine learning is an in-memory platform that is created for distributed and scalable machine learning.
With access to all client systems, developers can use the H2O machine learning framework to add data transformations and customized algorithms of their choice.
-Developed and designed for decision support system processes
-Widespread in risk and fraud propensity analytics, insurance customer analytics, and patient analytics in healthcare
-H2O-3 is compatible with Python, Java, JSON, R, Scala, and the Flow notebook/web interface
-Assimilates with Hadoop and Spark
-Highly versatile and extensible
-Provides quicker and more accurate predictive models
PyTorch machine learning framework is ideal for neural network design. It is based on Torch and Caffe2 which provide lots of customization options for optimizing algorithms, and it supports cloud-based software development.
It consists of multiple features, like libraries, tools, and distributed training. PyTorch machine learning framework supports Lua for user interface development.
It is integrated with Python and compatible with popular libraries like Numba and Cython. Also, it uses native Python code for model construction, supporting fast execution and better flexibility.
Quickly switches from development to graph mode, providing high performance and faster development in C++ runtime environments. It is easier and quicker to pick up for beginners.
-It is an open-source machine learning framework.
-Supports cloud-based software development
-Suitable for designing neural networks and Natural Language Processing
-Compatible with Numba and Cython
-Used by Meta and IBM
-Good for designing computational graphs
Apache MXNet was launched in the year 2017; it is the newest deep-learning framework. The best feature of Apache MXNet is it can support various languages, such as Python, C++, Java, Julia, Matlab, Go, R, Scala, Perl, and Wolfram Language.
It is a cutting-edge deep learning framework that supports convolutional neural networks (CNNs) and long short-term memory networks, among other cutting-edge deep learning models (LSTMs).
Apache MXNet allows users to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices.
-Open-source deep learning software framework
-used for training and deploying deep neural networks
-the library of MXNet is portable and lightweight.
-Highly scalable, promotes fast model training
-Flexible programming model supports multiple programming languages
7.Open Neural Networks Exchange (ONNX)
Open Neural Network Exchange or ONNX project is the brainchild of Microsoft and Facebook.
In the year 2019, it was recognized as a graduate project in Linux Foundation Artificial Intelligence (LFAI).
It is designed for the development and presentation of machine learning and deep learning models. ONNX caries models trained in various deep learning frameworks. In simple terms, you can train models in one framework and transfer it to another for inference.
ONNX is an intelligent system for switching between different machine learning frameworks such as PyTorch and Caffe2.
-Used for interpretability of models trained in various deep learning frameworks.
-Offers a definition of an extendable computation graph model.
Keras is an advanced framework that offers an interface for machine learning and deep learning processes. The library is highly productive and mainly focuses on the process of resolving issues that are related to deep learning.
The Keras machine learning framework can run models on your system’s Central Processing Unit (CPU) and Graphics Processing Units (GPU) simultaneously, as well as accelerate the training process.
Keras programming language is Python. It is composed of simple syntax and modular programming, which makes it beginner-friendly.
The application programming interface of Kera is simple, it shows outstanding flexibility, and it can operate on top of other open-source frameworks like Microsoft CNTK and Theano.
-Flexible, containing pre-defined functions, variables, and modules that make computing codes easier.
-Build custom layers for the neural networks.
-Mobile apps of Uber, Netflix, and Square use Keras's machine learning framework to build deep learning models.
-Consists of numerous pre-labeled datasets that allow users to access and training easier.
Theano machine learning framework programming language library is Python. It allows users to write, optimize, and evaluate mathematical expressions.
Theano has multidimensional arrays and it lets users optimize mathematical performance, mostly in deep learning with efficient machine learning algorithms.
It carries out symbolic differentiation efficiently by using GPUs. Many popular frameworks, like Keras and TensorFlow, are based on Theano.
It is possible to use Theano to reach speeds comparable to those of hand-crafted C implementations when dealing with issues containing a considerable amount of data.
Here are some features of Theano mentioned below:
-Supports Python programming language
-Uses GPUs and carries out symbolic differentiation efficiently
-Allow users to optimize mathematical performances
-Large-scale computationally thorough operations are the USP of Theano.
Caffe is a deep learning framework that comes with a pre-loaded set of trained neural networks.
It is extensively used in research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia.
To optimize with minimize the codding efforts, the users can toggle between the Graphic processing units (GPU) and Central processing unit (CPU) and model.
-Flexible, fast, and expressive framework for deep learning.
-Provides complete support for modeling of CNN (convolutional neural networks)
-Handle strong visual processing of images; it can process over 60M images per day with a single NVIDIA K40 GPU
Courses to checkout
The article explains the top machine learning frameworks and tools that are helpful in making the process of developing algorithms easier and more flexible.
Many businesses have developed their own machine-learning frameworks to provide service inside their operations.
If you are looking for a course in Artificial Intelligence and Machine Learning, reach out to us to sign up for the Artificial Intelligence and Machine Learning Master Program.
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