Best Machine Learning Frameworks(ML) for Experts in 2020

By | December 5, 2020
Machine Learning Frameworks

The ability of machines to be able to learn without being programmed, and improve over each iteration, through experience is called machine learning. ML is said to be a part of AI which is used for data science. For example, natural language processing, pattern recognition, image classification, clustering and regression are all different flavours of machine learning. Machine learning is extensively used for performing data science and helps businesses in predictive data analysis, making business decisions and forecasting the future. Check more about Best Machine Learning Frameworks below.

Best Machine Learning Frameworks (ML)

Machine learning can be performed through a set of algorithms. These algorithms can be divided into three categories:

Vamware
  • Supervised Machine Learning: In this type of learning, a machine is trained by giving a set of data and the expected outcome, i.e. we already label the data. The machine builds a model based on the training data set. Once the model is built, another data set (testing data set) is fed to the machine to check the accuracy of the model. Examples of supervised algorithms are classification algorithms like Support Vector Machines, Decision tree, Linear classifier and regression algorithms like linear regression, polynomial regression. Learn more
  • Unsupervised Machine Learning: in this type of algorithm, no training is given to the machine. The machine finds similarities and patterns in data by itself (without any form of supervision). Unsupervised learning is more complex than supervised learning methods. Some examples are clustering algorithms like K-means clustering, DBScan, dimensionality reduction etc. and Association rules like Apriori and ECLAT. Learn more
  • ReinforcementMachine Learning: the third primary type of machine learning and the most complex of all, Reinforcement learning involves an agent that has to take actions in a specific environment to ensure maximum rewards. The reward is cumulative and is based on some rules. Game theory, information theory, multi-agent systems, statistics etc. use reinforcement learning. It is also referred to as neuro-dynamic or approximate dynamic programming. Markov Decision Process and Q learning are vital learning models in reinforcement learning.

The relation between Machine Learning and Deep Learning

Just like Machine learning is a subset of AI, deep learning is a subset of machine learning, which works well for humongous datasets, and needs many layers of neural networks to reach the final output. Some examples are speech recognition, image recognition, self-driving cars, virtual assistants like Alexa, Cortana, Siri, Fraud detection etc.

relation between machine learning and deep learning

Key features of a framework

Some features of a machine learning framework are:

  • Frameworks are optimized for performance
  • Easily understandable making coding easier
  • Most frameworks are open-source, hence making them developer-friendly
  • ML frameworks allow for parallel processing
  • Reduces the overall complexities of machine learning, thus ensuring faster development

15 Top Machine Learning Frameworks

There are many machine learning frameworks and deep learning algorithms. We will discuss some of the most widely used algorithms that solve most of our real-life problems.

1. TensorFlow

TensorFlow

TensorFlow is an open-source machine learning, deep learning framework developed by Google. Its libraries use graphs that are used to describe workflows and complex networks in a simple manner. TensorFlow is used for both research projects and real-world applications. The most robust feature of TensorFlow is its ability to create neural networks. TensorFlow works on Windows, Mac and Linux operating systems and gives high performance because of its GPU (Graphics Processing Unit). TensorFlow was built using the C++ programming language. However, it supports Python, Java & C as well. TensorFlow is used for image recognition, image search, language translation, speech recognition, and creation of unique art.

 

2. Keras

Keras

Keras is a deep learning framework that has broad community support. It is written in Python and supports many neural-network computation engines, especially Convolutional Neural Networks. Keras is used along with low-level computation libraries like TensorFlow as a high-level API using tensorflow.keras. Keras offers a much easier to understand and use interface compared to TensorFlow. The other ‘Backend’ engines/libraries that support Keras are Theano & CNTK.

Developers can create new modules as a set of classes and functions using Python. The existing stand-alone modules can be used to create new models. With Keras, code deployment is fast, and it supports many devices like iOS, Cloud engine, Raspberry Pi etc. Keras can be used with single or multiple GPUs at the same time.

The base of Keras is ‘Model’ which defines the whole network graph. You can use the same model, or add more layers to it to suit your requirements. A simple sequential model can be built using the following steps:

  • Creating a sequential model
  • Convolution layer
  • MaxPooling layer
  • Dense layer
  • Dropout layer
  • Network Compilation, training and evaluation

Keras is used for face recognition, computer vision and many other use cases that require deep neural networks.

3. Scikit learn

Scikit-Learn

Perhaps the most widely used and useful general-purpose machine learning framework, scikit-learn contains all the tools for statistics, for example, classification, regression, dimensionality reduction, clustering etc. It is free and contains almost all the popular algorithms like Naïve Bayes, Decision tree, KNN, SVM etc. Scikit has been written in Python, C and C++ and was built on top of the SciPy ML library. Some unique features of scikit-learn are:

  • Can be easily reused
  • Free, open-source and commercially usable (BSD license)
  • Along with ML algorithms, it also has pre-processing, cross-validation and visualization algorithms.
  • Excellent documentation and community support

Scikit-learn is more like a high-level API for building standard machine learning models. Unlike TensorFlow, it doesn’t come with an automatic differentiation toolkit. Also, scikit-learn is apt for building quick algorithms, whereas TensorFlow requires more training time, thus making it more suitable for deep learning networks.

4. H2O.ai

h2o

H2o.ai is an open-source platform that aims to make ML easier for business users. Business users need not worry about deployment, performance tuning and other low-level tasks and can gain insights from data. H2o.ai is a suite of products:

  • H2o – the main product, which is an open-source, distributed, an in-memory platform for ML and predictive analysis. It can build production-ready models for supervised and unsupervised algorithms like classification, clustering. H2o has a simple yet effective UI known as flow.
  • Deepwater – as the name suggests, H2o combines with TensorFlow, Caffe and MXNet for more efficient processing using GPU based deep learning models.
  • Sparkling water – integration of H2o with Spark so that it is easier for existing Spark customers to use H2o capabilities.
  • Steam – this is an enterprise offering where data scientists can build, train and deploy ML models that can be accessed by various applications via APIs.
  • Driverless AI – Though named so, it is not specific to autonomous driving but also performs automatic feature engineering, model selection, tuning and ensembles, and helps in calibrating parameters that optimize the algorithms for specific use-cases.

H2o also provides REST API for external programs to access its capabilities over HTTP via JSON. H2o also supports algorithms other than supervised and unsupervised algorithms, like the quantile, early stopping, Word2Vec.

5. Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit

Abbreviated as CNTK, Microsoft Cognitive toolkit is an open-source kit for commercial level distributed deep learning. It is fast, scalable and reliable. Microsoft products like Skype, Cortana, Bing etc. use CNTK to develop commercial-grade systems using sophisticated algorithms. It offers built-in components like CNN, RNN, FFN, reinforcement learning, supervised and unsupervised learning, LSTM, generative adversarial networks and many more. Developers can add their custom components using Python on the GPU. The kit offers automatic hyperparameter tuning. The GPU Memory can fit large models and still maintain speed, efficiency and parallel processing.

The toolkit offers both high-level and low-level API to suit different business needs. You can evaluate the models using C++, C#, BrainScript and Python. You can also train and host the models on the Azure cloud and even add real-time training.

6. MXNet

MXNet

MXNet is a dynamic and flexible library for deep learning. It is short for mix and maximize. It supports many programming languages like Python, R, C++, Java, JavaScript, Julia, Perl, MATLAB etc. The library is portable and scalable to multiple GPUs and machines. Both MS Azure and AWS cloud providers support MXNet. Because of its ability to distribute processing into multiple GPUs, mxnet offers high speed and performance. MXNet supports many tools and libraries to build applications in the field of computer vision, natural language processing, time series and many more. It also supports automatic differentiation, i.e. automatically calculates the gradients required for training a model.

Mxnet has a hybrid front end, i.e. the Gluon Python API that lets developers switch to a symbolic mode for higher performance and faster execution.

7. Apple’s Core ML

Apple’s Core ML

When it comes to ML on a mobile device, Core ML comes as one of the most important frameworks for iOS and all other Apple devices like macOS, watchOS and tvOS. It can produce real-time results with low latency. This is especially useful for real-time prediction of live videos (successive frames) or images on the device. The application can run in offline mode (without network or API calls) without any loss of data. Some of the popular applications of Core ML are in real-time image recognition, face detection, text prediction, speaker identification etc. The process is simple. The machine learning models are pre-trained on cloud and then changed to Core ML format, which can be directly added to the required Xcode project. Some frameworks that support Core ML are vision, GamePlaykit and NLP.

8. Apache Mahout

Apache Mahout

Mahout is designed for data scientists, mathematicians and statisticians for designing their algorithms using the out-of-the-box distributed backend. It is a linear algebra framework and contains libraries in Java/Scala for all the maths operations required for machine learning. It has implementations for many algorithms like clustering, classification, and collaborative filtering using the map/reduce paradigm. However, the framework is not restricted to only Hadoop based implementations and also supports non-Hadoop clusters.

Mahout provides:

  • Framework for data mining tasks on huge volumes of data
  • Large data sets can be analyzed in a very short time
  • The clustering implementations are based on map-reduce – example, mean-shift, k-means, canopy, Dirichlet etc.
  • In-built libraries for vectors and matrix operations
  • Supports both complementary and distributed Naïve Bayes classification implementations

Many companies use Mahout for mining and modelling purposes. For example, Twitter uses it for interest modelling. Mahout algorithms can be used for many applications like gaming, data mining, pattern recognition, expert systems, robotics and many other areas.

9. Massive Online Analysis

Massive Online Analysis

MOA is a framework software that can build and run data mining and machine learning experiments on evolving data streams. It is free, open-source and contains almost all the popular machine learning algorithms like Bayesian classifiers, Decision tree, bagging, boosting, perceptron, regression, clustering, recommender system, pattern mining etc. The framework comes with a set of stream generators and learners that can be used on the command line, Java API as well as the GUI.

MOA is especially suitable for real-time data streams and situations prone to concept drift, for example, weather prediction, fraud detection or shopping preferences of a customer through change detection algorithms. It is a much-suited framework for large-scale machine learning. The framework models concept drift (i.e. change in the statistical value of the target variable, leading to inaccuracy in predictions) as a weighted union of two pure distributions that characterize the target variable (concept) before and after the change (drift).

Datastream environment is a more challenging environment when compared to the traditional batch learning environment because a task can be processed only once within a limited time and using only limited memory. The algorithm may need to perform prediction at any stage.

MOA is written in Java, making it portable and platform-independent. It is also related to WEKA, which has implementations for many algorithms so that MOA classifiers and streams can be used from WEKA and WEKA classifiers can be called from MOA.

10. Caffe

Caffe

Caffe is an expressive deep learning framework made for speed and modularity. Caffe was earlier available under the BSD 2-Clause license, and Facebook open-sourced it in 2017. Caffe was integrated with Apache Spark to produce a distributed deep learning framework, namely CaffeOnSpark. It has been written in C++ with Python and MATLAB programming interfaces.

Caffe is much suitable for industry deployment like vision, speech etc. as well as research projects. It can process more than 60 million images in a day on a single GPU – that’s quite fast!

The expressive nature of Caffe leads to innovation, and in just one year of its release, Caffe has been used by 1000 developers who could contribute their bits to further improve the framework. Due to this, Caffe already gained a huge active community.

Caffe is suitable for major deep learning networks like Convolutional Neural Networks, Long Short-term memory, Long-term Recurrent Convolutional Network and fully connected neural networks. Caffe stores data in the form of blobs. How the information is stored across various neural networks layers is defined in the properties of the blob.

Caffe is most suitable for mobile devices and somewhat computationally constrained platforms.

Bonus Machine Learning Frameworks

11. Azure ML Studio

Azure ML Studio

Azure ML Studio is a robust machine learning platform, especially for small and medium businesses without needing any special coding skills or hardware, thus saving enormously on cost and resources. Azure provides machine learning as a web service on a pay-as-you-go basis. This means that businesses can just purchase the services and immediately build their machine learning applications.

Due to its drag and drop components, there is minimal code required, and the interface becomes easy and flexible to use. Further, the configuration of properties is simplified. Azure ML studio works well for massive data sets and supports most ML algorithms like classification, regression, clustering and so on. A typical implementation of web service will look like:

Azure ML Studio

Apart from the flexibility and extensibility, ML studio also has the advantage of excellent community support and documentation. If the out-of-the-box algorithms are not sufficient, developers can add their implementations in R or Python. Azure ML studio is great for both descriptive and predictive analysis, and all the steps of data science can be done from data collection to evaluation of the model.

12. Amazon Machine learning

Amazon Machine learning

Amazon ML is a complete service that uses a set of products consisting of algorithms and mathematical models for developing predictive applications. The service reads data through Redshift, RDS and Amazon S3, and uses the Amazon ML API and AWS management console to visualize the data. S3 buckets can be used to import and export data to other AWS services.

AWS can be used for various algorithms that perform classification, dimensionality reduction, regression and reinforcement learning (through Amazon SageMaker RL). It is suitable for a start for those working with other AWS services and just starting with ML because the choice of algorithms is limited (as of now). The main algorithm (model) for which AWS ML was built is the Stochastic Gradient Descent (SGD). Even with these constraints, AWS ML is becoming popular because it is easy to learn and implement and saves a lot of development time.

AWS optimized TensorFlow framework can achieve near-linear scalability and better utilization of GPU by improving the way tasks are distributed across GPUs.

Amazon ML and AI services are used for services like Amazon Comprehend, Amazon translate, computer vision, NLP and many more.

13. Theano

theano

Theano is an open-source project written in Python, CUDA and contains a compiler for manipulating and evaluating mathematical expressions, particularly matrix-based expressions (n-dimensional arrays). Theano is built on top of the Numpy library. Some unique features of Theano are:

  • Can handle complex computations required for large neural network algorithms
  • Supports both convolutional and recurrent neural networks
  • Extremely fast, even for data-intensive computations (About 140 times faster than CPU) through the use of GPU.
  • Dynamic code generation for faster evaluation of expressions like differentiation, matrix arithmetic, gradient computation etc.
  • Tools for unit testing, debugging and verifying bugs and potential issues
  • Tight integration with Numpy
  • Performs differentiation (computing derivatives) efficiently
  • Stable output expressions for large datasets

Theano builds an entire computation graph for the model and compiles the same into a highly efficient code by applying optimization techniques. A special operation, known as ‘function’ then pushes the compiled code into the Theano runtime. The ‘function’ is iteratively executed to train the neural network. The computation graph can be visualized using the printing package in Theano library.

14. Shogun

Shogun

The Shogun ML toolbox is open-source software written in C++. It offers a wide range of algorithms and data structures for different machine learning problems. The framework also offers interfaces for Python, R, Java, Ruby, MATLAB, C# and Octave. The main reason Shogun was created was for kernel-based algorithms like Support Vector Machines used for both classification and regression. It provides a generic interface to 15 SVM implementations like SVMLin, SVMlight, GPDT, LibSVM, LibLinear etc. The SVMs can be easily combined with over 35 different kernel functions.

Apart from kernel functions, Shogun also supports dimensionality reduction algorithms like PCA, multidimensional scaling, diffusion maps, Laplacian Eigenmaps etc., online learning algorithms, clustering algorithms like k-Means and GMM, hidden Markov models, linear discriminant analysis, perceptron and many more.

Shogun provides an implementation for many kernels like gaussian, polynomial, sigmoid or linear kernels for numerical data, and weighted degree, spectrum and weighted degree with shifts for a special type of data. An example of special data could be DNA sequence.

Shogun is accessible on the browser via the cloud as well. It also provides many data representations and state-of-the-art tools for rapid prototyping of data pipelines.

15. Torch

Torch

Torch is a popular scientific computing framework that computes using GPUs, making it fast and efficient. It uses LuaJIT scripting language with C/CUDA underlying implementation. Torch makes scientific computing easy and accessible and offers high flexibility. There is tremendous community support, and there are many dedicated packages for image networking, parallel processing, computer vision, signal processing, audio and video networking etc. It is built on top of the Lua community. Torch provides a lot of routines for slicing, indexing, resizing, transposing and other data processing methods, and consists of a powerful n-dimensional array.

Torch is open-source, and the core package is known as ‘torch’. The tensor supports all major mathematical operations like the dot product, matrix-vector product, matrix-matrix multiplication, and statistical distributions like normal, uniform, multinomial.

The nn package in Torch is used for neural networks. It supports both feedforward and backpropagation operations using the respective methods. PyTorch is a Python implementation of Torch framework, which provides dynamic graphs, fast computation and a host of libraries for machine learning tasks.

Torch is extensively used by Facebook for its AI and has been extended for use in mobile devices (iOS and Android). IBM, Twitter, Idiap, Google, Nvidia and Yandex also use Torch for research purposes.

Google Cloud ML engine

Not to be confused with an ML framework, Google’s cloud ML engine is a hosted platform that can perform training and predictions. You can use the service to either train the model using other ML frameworks like Keras, XGBoost, scikit-learn, TensorFlow, etc., or make predictions or do both. The trained model can be hosted on various other environments, including public cloud, external environments and on-premises infrastructure. The engine can also perform hyperparameter tuning that evaluates the accuracy of results. Google Cloud ML engine takes in already prepared data into its Google Cloud storage bucket. ML engine then initiates distributed training jobs that take in the data from the bucket. Data can be prepared using other Google services on Google cloud itself, or it can be externally prepared data being uploaded to the cloud.

How to choose the most suitable framework?

It depends on what your business use case is. As we have seen above, each framework has its unique strengths and can serve different purposes. For example, for building quick algorithms and models, you can use a general-purpose framework like scikit-learn. For more complex calculations and data-intensive operations, Theano or TensorFlow are the best options. If you have less time to build a deep learning network, use Azure ML studio. Caffe is another framework that performs fast processing and can be integrated with other frameworks to give more.

Learning machine learning frameworks through books

Many books cover machine learning in detail; however, these two books cover a bit more advanced topics and will guide you whether you are a beginner, intermediate or expert level machine learning engineer.

  1. Pattern Recognition and Machine learning by Christopher Bishop
  2. Hands-on Machine learning with Scikit-learn, Keras and TensorFlow by Aurelien Geron

Conclusion

In this article, we have learnt about the most important and widely used machine learning frameworks. This list is in no way complete, and many other frameworks are useful too. Frameworks can significantly simplify your code so you can focus on the business logic. Just to recap, TensorFlow, Caffe, CNTK are some brilliant frameworks for deep learning, frameworks like scikit-learn, MOA is great for generic machine learning and cloud-based Google Cloud ML, Amazon ML, Azure ML studio are great for clouds! You don’t necessarily have to be a developer (coder) and still work on these frameworks. Let us know in the comments section if you would like us to include any other framework in the above list.

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