AI News, Some Lesser-Known Machine Learning Libraries
Some Lesser-Known Machine Learning Libraries
As promised, we have come up with yet another list of some lesser known Machine Learning Libraries that you might find interesting.
Opt can generate different variations of the solver that helps the users to easily explore tradeoffs in numerical precision, matrix-free methods, and solver approaches.
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.
LIBIRWLS is an integrated library that makes use of a parallel implementation of the Iterative Re-Weighted Least Squares (IRWLS) procedure for solving the quadratic programmig (QP) problem that arises during the training of Support Vector Machines (SVMs).
scikit-feature is an open-source feature selection repository in Python built upon one widely used machine learning package scikit-learn and two scientific computing packages Numpy and Scipy.
Libmvec is vector math library added in Glibc 2.22 to support SIMD constructs of OpenMP4.0 This is Cython implementation of k-MC2 and AFK-MC2 seeding for K nearest neighbhour clustering algorithm.
How to break a CAPTCHA system in 15 minutes with Machine Learning
Everyone hates CAPTCHAs — those annoying images that contain text you have to type in before you can access a website.
Time elapsed so far: 2 minutes Before we go any further, let’s mention the tools that we’ll use to solve this problem: Python 3 Python is a fun programming language with great libraries for machine learning and computer vision.
To break a CAPTCHA system, we want training data that looks like this: Since we have the source code to the WordPress plug-in, we can modify it to save out 10,000 CAPTCHA images along with the expected answer for each image.
After a couple of minutes of hacking on the code and adding a simple ‘for’ loop, I had a folder with training data — 10,000 PNG files with the correct answer for each as the filename: This is the only part where I won’t give you working example code.
Time elapsed so far: 5 minutes Now that we have our training data, we could use it directly to train a neural network: With enough training data, this approach might even work — but we can make the problem a lot simpler to solve.
So we’ll start with a raw CAPTCHA image: And then we’ll convert the image into pure black and white (this is called thresholding) so that it will be easy to find the continuous regions: Next, we’ll use OpenCV’s findContours() function to detect the separate parts of the image that contain continuous blobs of pixels of the same color: Then it’s just a simple matter of saving each region out as a separate image file.
Sometimes the CAPTCHAs have overlapping letters like this: That means that we’ll end up extracting regions that mash together two letters as one region: If we don’t handle this problem, we’ll end up creating bad training data.
In that case, we can just split the conjoined letter in half down the middle and treat it as two separate letters: Now that we have a way to extract individual letters, let’s run it across all the CAPTCHA images we have.
Here’s a picture of what my “W” folder looked like after I extracted all the letters: Time elapsed so far: 10 minutes Since we only need to recognize images of single letters and numbers, we don’t need a very complex neural network architecture.
Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe
From healthcare and security to marketing personalization, despite being at the early stages of development, machine learning has been changing the way we use technology to solve business challenges and everyday tasks.
But now let’s look at free and open source software that allows everyone to board the machine learning train without spending time and resources on infrastructure support.
For a business that’s just starting its ML initiative, using open source tools can be a great way to practice data science gratis before deciding on enterprise level tools like Microsoft Azure or Amazon Machine Learning.
Open source ML tools also let you leverage transfer learning, meaning solving machine learning problems by applying knowledge gained after working with a problem from a related or even distant domain.
Depending on the task you’re working with, pre-trained models and open datasets may not be as accurate as custom ones, but they will save a substantial amount of effort and time, and they don’t require you to gather datasets.
According to Andrew Ng, former chief scientist at Baidu and professor at Stanford, the concept of reusing open source models and datasets will be the second biggest driver of the commercial ML success after supervised learning.
Comparing GitHub commits and contributors for different open source tools Among many active and less popular open source tools, we’ve picked five to explore in depth to help you find the one to start you on the road to data science experimentation.
Some of the popular models you can apply are MNIST, a traditional dataset helping identify handwritten digits on an image, or Medicare Data, a dataset by Google used to predict charges for medical services among others.
Theano is a low-level library for scientific computing based on Python, which is used to target deep learning tasks related to defining, optimizing, and evaluating mathematical expressions.
For these reasons, Theano is mainly applied in combination with more user-friendly wrappers, such as Keras, Lasagne, and Blocks – three high-level frameworks aimed at fast prototyping and model testing.
However, considering that you probably won’t use Theano directly, its numerous uses expand as you use it as foundation for other libraries: digit and image recognition, object localization, and even chatbots.
Use cases Facebook used Torch to create DeepText, a tool categorizing minute-by-minute text posts shared on the site and providing a more personalized content targeting.
The Python NumPy-based ecosystem includes tools for array-oriented computing Datasets and models The library already includes a few standard datasets for classification and regression despite their being too small to represent real-life situations.
However, the diabetes dataset for measuring disease progression or the iris plants dataset for pattern recognition are good for illustrating how machine learning algorithms in scikit behave.
Moreover, the library provides information about loading datasets from external sources, includes sample generators for tasks like multiclass classification and decomposition, and offers recommendations about popular datasets usage.
Considering its simplicity and numerous well-described examples, it’s an accessible tool for non-experts and neophyte engineers, enabling quick application of machine learning algorithms to data.
One of the biggest benefits of the framework is Model Zoo – a vast reservoir of pre-trained models created by developers and researchers, which allow you to use, or combine a model, or just learn to train a model of your own.
Use cases By using the state-of-the-art Convolutional Neural Networks (CNNs) – deep neural networks successfully applied for visual imagery analysis and even powering vision in self-driving cars – Caffe allowed Facebook to develop its real-time video filtering tool for applying famous artistic styles on videos.
The number of machine learning tools appearing on the market and the number of projects applied by businesses of all sizes and fields create a continuous, self-supporting cycle.
Top 20 Python libraries for data science in 2018
Python continues to take leading positions in solving data science tasks and challenges.
This year, we expanded our list with new libraries and gave a fresh look to the ones we already talked about, focusing on the updates that have been made during the year.
It is intended for processing large multidimensional arrays and matrices, and an extensive collection of high-level mathematical functions and implemented methods makes it possible to perform various operations with these objects.
In addition to bug fixes and compatibility issues, the crucial changes regard styling possibilities, namely the printing format of NumPy objects.
The package contains tools that help with solving linear algebra, probability theory, integral calculus and many more tasks.
SciPy faced major build improvements in the form of continuous integration into different operating systems, new functions and methods and, what is especially important — the updated optimizers.
There have been a few new releases of the pandas library, including hundreds of new features, enhancements, bug fixes, and API changes.
The improvements regard pandas abilities for grouping and sorting data, more suitable output for the apply method, and the support in performing custom types operations.
Statsmodels is a Python module that provides many opportunities for statistical data analysis, such as statistical models estimation, performing statistical tests, etc.
Thus, this year brought time series improvements and new count models, namely GeneralizedPoisson, zero inflated models, and NegativeBinomialP, and new multivariate methods — factor analysis, MANOVA, and repeated measures within ANOVA.
As an example of an appearance improvements are an automatic alignment of axes legends and among significant colors improvements is a new colorblind-friendly color cycle.
The continuous enhancements of the library with new graphics and features brought the support for “multiple linked views” as well as animation, and crosstalk integration.
The library provides a versatile collection of graphs, styling possibilities, interaction abilities in the form of linking plots, adding widgets, and defining callbacks, and many more useful features.
Bokeh can boast with improved interactive abilities, like a rotation of categorical tick labels, as well as small zoom tool and customized tooltip fields enhancements.
With its help, it is possible to show the structure of graphs, which are very often needed when building neural networks and decision trees based algorithms.
It provides algorithms for many standard machine learning and data mining tasks such as clustering, regression, classification, dimensionality reduction, and model selection.
Gradient boosting is one of the most popular machine learning algorithms, which lies in building an ensemble of successively refined elementary models, namely decision trees.
These libraries provide highly optimized, scalable and fast implementations of gradient boosting, which makes them extremely popular among data scientists and Kaggle competitors, as many contests were won with the help of these algorithms.
PyTorch is a large framework that allows you to perform tensor computations with GPU acceleration, create dynamic computational graphs and automatically calculate gradients.
Therefore, dist-keras, elephas, and spark-deep-learning are gaining popularity and developing rapidly, and it is very difficult to single out one of the libraries since they are all designed to solve a common task.
Comparing to the previous year, some new modern libraries are gaining popularity while the ones that have become classical for data scientific tasks are continuously improving.
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Skymind bundles Deeplearning4j and Python deep learning libraries such as Tensorflow and Keras (using a managed Conda environment) in the Skymind Intelligence Layer (SKIL), which offers ETL, training and one-click deployment on a managed GPU cluster.
DL4J is a JVM-based, industry-focused, commercially supported, distributed deep-learning framework that solves problems involving massive amounts of data in a reasonable amount of time.
For more information on benchmarking Deeplearning4j, please see this benchmarks page to optimize its performance by adjusting the JVM’s heap space, garbage collection algorithm, memory management and DL4J’s ETL pipeline.
PyTorch offers dynamic computation graphs, which let you process variable-length inputs and outputs, which is useful when working with RNNs, for example.
Since it’s introduction, PyTorch has quickly become the favorite among machine-learning researchers, because it allows certain complex architectures to be built easily.
Some version of it is used by large tech companies such as Facebook and Twitter, which devote in-house teams to customizing their deep learning platforms.
Torch, while powerful, was not designed to be widely accessible to the Python-based academic community, nor to corporate software engineers, whose lingua franca is Java.
And we believe that a commercially supported open-source framework is the appropriate solution to ensure working tools and building a community.
Pros and Cons: Pros and Cons Caffe is a well-known and widely used machine-vision library that ported Matlab’s implementation of fast convolutional nets to C and C++ (see Steve Yegge’s rant about porting C++ from chip to chip if you want to consider the tradeoffs between speed and this particular form of technical debt).
In contrast to Caffe, Deeplearning4j offers parallel GPU support for an arbitrary number of chips, as well as many, seemingly trivial, features that make deep learning run more smoothly on multiple GPU clusters in parallel.
(As of March 2016, another Theano-related library, Pylearn2, appears to be dead.) In contrast, Deeplearning4j brings deep learning to production environment to create solutions in JVM languages like Java and Scala.
This license does not apply to the method by which CNTK makes distributed training easy – one-bit SGD – which is not licensed for commercial use.
Chainer is an open-source neural network framework with a Python API, whose core team of developers work at Preferred Networks, a machine-learning startup based in Tokyo drawing its engineers largely from the University of Tokyo.
Until the advent of DyNet at CMU, and PyTorch at Facebook, Chainer was the leading neural network framework for dynamic computation graphs, or nets that allowed for input of varying length, a popular feature for NLP tasks.
On a business level, Gluon is an attempt by Amazon and Microsoft to carve out a user base separate from TensorFlow and Keras, as both camps seek to control the API that mediates UX and neural net training.
That is, anyone is free to make and patent derivative works based on Apache 2.0-licensed code, but if they sue someone else over patent claims regarding the original code (DL4J in this case), they immediately lose all patent claim to it.
(In other words, you are given resources to defend yourself in litigation, and discouraged from attacking others.) BSD doesn’t typically address this issue.
That is, we automate the setting up of worker nodes and connections, allowing users to bypass libs while creating a massively parallel network on Spark, Hadoop, or with Akka and AWS.
(When we talk about operations, we also consider things like strings and other tasks involved with higher-level machine learning processes.) Most deep-learning projects that are initially written in Python will have to be rewritten if they are to be put in production.
Many Python programmers opt to do deep learning in Scala because they prefer static typing and functional programming when working with others on a shared code base.
Finally, Java is a secure, network language that inherently works cross-platform on Linux servers, Windows and OSX desktops, Android phones and in the low-memory sensors of the Internet of Things via embedded Java.
While Torch and Pylearn2 optimize via C++, which presents difficulties for those who try to optimize and maintain it, Java is a “write once, run anywhere” language suitable for companies who need to use deep learning on many platforms.
In sum, Java boasts a highly tested infrastructure for pretty much any application, and deep-learning nets written in Java can live close to the data, which makes programmers’ lives easier.
- On Monday, June 24, 2019
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