Machine learning has become the center of discussion in artificial intelligence today.
It touches all fields, including engineering, medicine, business, social science, and more.
Keras
Kerasis part of TensorFlow’s extensive machine learning utilities.
But it’s different in that it’s a higher-level API that ships with TensorFlow.
Plus it’s more human-friendly and written with Python.
So it’s more implementable as it offers concise documentation that’s easy for machine-learning beginners to narrow down.
The library, however, spans across text and image-based algorithms for training and testing your dataset.
So you’ll hardly need to branch out to borrow utilities from other libraries.
you could always switch to that.
So it lets you simultaneously run CPU calculations along with GPU processing asynchronously.
TensorFlow
Introduced by Google in 2015,TensorFlowis more of a framework than a library.
It’s an open-source library built with C++, and it works by tracking dataflow graphs.
Related:What Is TensorFlow Lite and How Is It a Deep Learning Framework?
Although TensorFlow docs are comprehensive, unlike Keras, they may be too diversified for beginners to comprehend.
That said, it has solid community support, and you’ll also find manyopen-source TensorFlow examplesout there.
An advantage of TensorFlow over Keras is that you could use TensorFlow directly without Keras.
Mlib Spark
Here’s something pretty handy from Apache Spark.
Released and made open-source in 2010,Mlib Sparkuses iterative calculations to run machine learning algorithms.
Because of its iterative nature, Mlib can make use of Hadoop or local data sources and workflows.
Plus, it’s capable of running complex logic within a short period.
Ultimately, it’s still one of the fastest machine learning libraries out there.
It also excels in terms of data preprocessing and pattern mining.
Related:What Are Machine Learning Algorithms?
Mlib Spark is an embed of Spark itself, so it upgrades with every Spark release.
Mlib Spark has explanatory documentation, so a beginner can easily pick it up.
4. mlpack
mlpackwas released in 2008 and developed with C++ using a linear algebra library called Armadillo.
Thus, scalability with other languages besides C++ is often an issue with mlpack.
The documentation has easy-to-follow guides and examples that are available for various programming languages.
Pytorch
Facebook developedPytorchand released it officially in 2016.
Like Keras and Tensorflow, Pytorch supports the CPU processing of datasets.
And if your dataset is large, it features a GPU processor to handle your calculations.
Plus, it’s tensor-based.
In addition to Python, the library supports binding for both C++ and Java.
Pytorch, in addition to other utilities, offers subsidiary libraries includingtorchvision,torchtext,torchaudio, andTorchServe.
Pytorch also lets you transform your datasets into a machine-friendly format.
So it’s also a perfect library for preprocessing data.
Invariably, feature extraction, data cleaning, data splitting, and hyperparameter tuning are all possible with Pytorch.
Scikit-Learn
Immersively built with Python,scikit-learn, also calledsklearn, was publicly released in 2010.
In essence, scikit-learn supports both supervised and unsupervised models.
Overall, it’s more beginner-friendly than other libraries on the list.
And it also uses Matplotlib to present compelling story-telling visualizations.
Like Scikit-Learn, Theano depends on Numpy for executing numerical calculations.
The library supports GPU-based computations, plus it generates low-level C code.
This speeds up mathematical evaluations with Theano, no matter how large they are.
Additionally, its deep learning models run on tensors.
You may not get enough community support, though.
That’s because Theano isn’t as popular as the other libraries we mentioned earlier.
That doesn’t make it any less beginner-friendly.
The tutorial in the docs is easy to understand.
Its ability to simplify complex arrays and optimize infinite computations makes it perfect for creating scalable machine learning models.
Which Library Should You Use for Your Next Machine Learning Project?
But that said, familiarizing yourself with machine learning fundamentals through courses and tutorials is helpful.
