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Best Machine Learning Frameworks(ML) for Experts in 2020
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Digitalization is the bedrock of information technology and science. Businesses around the world are increasingly investing in digitalization at a frantic pace with Machine Learning (ML) and Artificial Intelligence (AI) witnessing significant adoption in day to day operations of organizations.
2020 is envisaged as a tipping point for Machine Learning adoption and percolation. Several Machine Learning Frameworks have been developed and are in vogue today. The selection of the right Framework for your business is a daunting exercise, and getting to know the best fit software could be a challenge. This article provides you with a good grip of Ml frameworks available today and could help you in your search for a right fit framework for your use or business.
Let us run by a brief concept of Machine Learning and some of the most used Machine Learning frameworks and in trend in 2020.
What is ML?
ML is an area of computational science that deals with the analysis and interpretation of patterns and structures in large volumes of data to help learn, reason, and to support business decision making without or with very little need for human interface. Machine Learning permits users to feed large volumes of data to a computer algorithm that will analyze and generate data-driven decisions and recommendations. If there are any errors or outliers in information identified, the algorithm is structured to take this new information as an input to improve its future output for recommendations and decision making.
Simply put, ML is a subset of AI that supports organizations to analyze data, learn, and adapt on an ongoing basis to help in decision making.
What is a Machine Learning Framework?
A simple definition could be to think of it as a tool or library that allows developers to build ML models or Machine Learning applications with ease without having to get into the nuts and bolts of the base or core algorithms.
Best Machine Learning Frameworks
Now let us dive into some of the well-known Machine Learning Frameworks in detail:
From the stable of Google, TensorFlow is a versatile and possibly the better one among Machine Learning tools. It provides extensive, flexible features, an exhaustive library for programming, classifications, regression models, neural networks, including a suite to write algorithms for software. It runs on CPUs and as well on GPUs. The key advantage of TensorFlow is that it can be used in two ways, Script Tags or by installation via NPM. The challenge with TensorFlow is that it is not learner-friendly for beginners.
As a knowledge seeker, your thirst for knowing the details has just begun. To learn more about TensorFlow, visit here - a true repository for technology geeks and knowledge seekers.
Shogun is an open-source Machine Learning framework that works well with C++. Again, it is free of cost and very useful for developers to design algorithms and data structures, specifically for ML problems in the field of education and research.
Shogun facilitates coders to connect with other ML libraries, which include LibLinear, LibSVM, SVMLight, LibOCAS, and many more.
Besides, Shogun is compatible with other languages and frameworks, such as R, Python, Java, Octave, C#, Ruby, MatLab, and Lua. The highlight of this Machine Learning framework is that it helps implement Hidden Markov models, process large volumes of data, user friendly, and provides several flexible features and functionalities.
3. Sci-Kit Learn
Sci-Kit Learn specifically supports development work in Python with an extensive library for Python programming language. It is rated by users as one of the best for data mining and data analysis. Sci-Kit Learn provides support for designing algorithms and models for classifications, clustering, pre-processing, regression, Dimensional reduction, and Model selection.
Sci-Kit Learn is an open-source platform, and it comes for free. It is learner-friendly even for amateur developers and comes with detailed documentation. Further, it permits developers to change any pre-set parameters for algorithms when in use or during run-time. The framework is better suited for unsupervised and administrative calculations.
PyTorch Machine Learning Framework, which is based on Torch and Caffe2, comes with loads of options for optimizing algorithms. The torch is perfectly suited to design neural networks using Autograd Module and Natural Language Processing. PyTorch is open-source and supports cloud-based software development. It comes with multiple features, including libraries, tools, and distributed training. For User Interface development, it supports Lua language. Pytorch is used by Facebook and IBM. The torch is rated as the best for designing computational graphs, and its front end development is hybrid, which makes usability very versatile. The platform was designed to be integrated with Python and compatible with libraries such as Numba and Cython.
Microsoft is the owner of CNTK, is used to describe neural networks as a sequence of computational development steps in the form of directed graphs. It is an open-source ML framework designed with algorithms in C++ programming language and production readers. CNTK is very robust for large-scale, multi-dimensional, or sparse data sets from C++, Python, and BrainScript. It supports developers to merge and view various ML model types, which includes recurrent networks, feed-forward deep neural networks, and convolutional neural networks.
6. Apache MXNet
H2O is an open-source ML framework developed to cater to organizations in their decision support system processes. H2O is widely deployed for Risk and Fraud Propensity Analytics, Insurance Customer Analytics, Patient Analytics in Healthcare, Advertising Spend and ROI, and customer intelligence.
H2O has undergone three avatars, and the current version is H2O – 3. H2O -3 is an in-memory platform that is designed for distributed, scalable ML. H2O-3 is compatible with interfaces such as Java, JSON, R, Python, Scala, and the Flow notebook/web interface. It also integrates seamlessly with big data technologies such as Hadoop and Spark. H2O-3 delivers implementations of many popular algorithms such as GBM, Random Forest, Deep Neural Networks, Word2Vec, and Stacked Ensembles.
H2O-3 is highly versatile and extensible, and developers can append data transformations and add customized algorithms of their preference and have access to all client systems. To put it in simple terms - Data collection is fairly easy, but Decision making is tough. H2O-3 is just for that, and it makes it quick and easy to help derive insights from voluminous data through quicker and more accurate predictive models.
8. Apple’s Core ML
Core ML was primarily developed for macOS, iOS, and TVOS applications and is highly learner-friendly, which includes fresher or out of school developers.
Core ML is very comprehensive and provides a plethora of features such as image classification, sentence classification, natural language processing, barcode detection, Gameplay Kit, and object tracking. With its unique low-level tech stack, it can provide near extreme performance by factoring in CPUs and GPUs. Concerning security and ease of use, Core ML ensures user privacy and functionality of the app even in offline mode.
So, how do you identify and choose your best fit ML Framework?
Your preference and selection of a Machine Learning framework are dependent on the specific algorithms that the framework will run and the procurement budget. Sure, from this article, you have got a good understanding of what ML and ML frameworks are. Should you need more relevant information and insights on ML and ML frameworks, you may click on the provided links.
Types of Machine Learning
1. Supervised Machine Learning
Supervised Machine Learning algorithms attempt to build relationships and dependencies between target predictive output and input features so that the output values can be predicted for new data sets based on past learning in relationships and dependencies from previous data sets.
It is important here that human experts act as teachers, and training data is fed to a computer algorithm containing the input predictors. The computer is shown the correct output. From output data, the computer algorithm can learn patterns. Supervised Machine Learning uses Predictive Models and Labeled data.
The major type of Supervised ML typically addresses regression and classification problems. Some of the common algorithms that Supervised ML handle is Nearest Neighbor, Naive Bayes, Decision Trees, Linear Regression, Support Vector Machines (SVM), and Neural Networks.
2. Unsupervised Machine Learning
Here, the system is trained using unlabeled data. There is no human interface or teacher whatsoever, and the computer might be capable of teaching humans or revealing new outputs based on its learning from patterns in data. This is a classic example where the algorithms are helpful in situations where the user or expert doesn’t exactly know what he wants from the data.
Further, the Machine Learning algorithms are majorly utilized in pattern detection and descriptive modeling, where there are no output labels. The algorithms use mining techniques on the input data to determine rules, detect patterns, and create a summary group of data points that helps to derive insights and describe data meaningfully and with a better purpose to users. Unsupervised ML uses Descriptive Models, and majorly includes Clustering Algorithms and Association Rule Learning Algorithms.
Facebook is a classic example of unsupervised ML. The ML Framework of Facebook detects hidden patterns and uses facial recognition features in photos and pictures to recommend new connections etc. for users.
3. Reinforcement Machine Learning
Reinforcement ML is a method that aims at using observations collected from interactions with the immediate environment for taking actionable decisions that could help in maximizing returns and benefits or minimize risk. The reinforcement learning algorithm also is known as “the agent,” learns continuously and in an iterative manner from the environment. During the process, the agent goes through experiential learning from the environment until it explores all the possible states before suggesting recommendations or decisions.
Now, let us see some example algorithms for Reinforcement Learning. These are Model Free Algorithm (MFA) as against a model-based algorithm where a typical transition probability distribution and reward functions with relevance to the Markov decision process (MDP) is used. An MFA is capable of handling dilemmas using stochastic transitions and rewards and without the requirement of any adaptations. The common and most used algorithms are:
- Monte Carlo
Note that the above is only a list of sample algorithms. These algorithms are a little complex to be explained in a few words since these have wide and deep applications in data science. As a knowledge seeker, you need to master the various algorithms and their approaches, and its application in programming to get your desired outcomes. To delve into and dwell in Machine Learning technologies and techniques recommend you visit here.
Machine Learning has set the stage for automated systems. What we have seen in this article is only the surface. While machine learning is not a new technique, the sudden exponential boom has come on the backdrop of significant performance breakthroughs in terms of accuracy in areas such as speech and language recognition, and computer vision. Few important factors have contributed to the growing successes of ML - large volumes of digital images, speech files, video, and text that are available on public domain to researchers and technologists looking to train ML systems.
Aside, the easy availability of large parallel processing power brought on by GPUs has seamlessly integrated well with the ecosystem to form large ML powerhouses. Today anyone with an internet connection can use these ML systems and models that are available via cloud services provided by firms like Amazon, Google, and Microsoft.
With the increasing use of machine learning, organizations and businesses are now creating CoEs in ML to accelerate adoption and adaption of ML. Very soon, ML will become so ubiquitous that it will touch every industry on this planet.
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