The AltexSoft data science team joined the discussion, too.And if you’re looking for a particular type of machine learning tools, just skip to your sector of interest:Machine learning languages Data analytics and visualization tools Frameworks for general machine learning Frameworks for neural network modeling Big data toolsYou’re in an ethnic restaurant and you’re not familiar with the culture..It’s ranked the seventh most popular language (38.8 percent), and now is one step ahead of C# (34.4 percent).Head of research in Respeecher Grant Reaber, who specializes in deep learning applied to speech recognition, uses Python as “almost everyone currently uses it for deep learning. Swift for TensorFlow sounds like a cool project, but we will wait until it’s more mature before thinking about using it,” concludes Grant.Co-founder of the NEAR.AI startup who previously managed a team in Google Research on deep learning for NLU Illia Polosukhin also sticks with Python: “Python was always a language of data analysis, and, over the time, became a de-facto language for deep learning with all modern libraries built for it.”One of the use cases of Python machine learning is model development and particularly prototyping.Data science competence leader at AltexSoft Alexander Konduforov says he uses it primarily as a language for building machine learning models.Vitaliy Bulygin, the lead engineer at Samsung Ukraine, considers Python one of the best languages for fast prototyping..“During prototyping, I find the optimal solution and rewrite it in a language required for a project, for example, C++,” explains the specialist.Facebook AI researcher Denis Yarats notes that this language has an amazing toolset for deep learning like PyTorch framework or NumPy library (which we’ll discuss later in the article).C++ is a flexible, object-oriented, statically-typed language based on the C programming language..And since C++ is clean enough for the explanation of basic concepts, it’s used for research and teaching.Data scientists use this language for diverse yet specific tasks. Andrii Babii, a senior lecturer at the Kharkiv National University of Radioelectronics (NURE), uses C++ for parallel implementations of algorithms on CUDA, an Nvidia GPU compute platform, to speed up applications based on those algorithms.“I need C++ when I write my custom kernels for CUDA,” adds Denis Yarats.R, a language and environment for statistics, visualizations, and data analysis, is a top pick for data scientists..The language allows for creating high-quality plots, including formulae and mathematical symbols.Alexander Konduforov notes machine learning with R enables fast data analysis and visualizations.It’s time to talk a little about Python pandas, a free library with the cutest name..Data science devotee Wes McKinney developed this library to make data analysis and modeling convenient in Python..Through the ability to define arbitrary data types, NumPy easily and quickly integrates with numerous kinds of databases.scikit-learn is an open source Python machine learning library build on top of SciPy (Scientific Python), NumPy, and matplotlib.Initially started in 2007 by David Cournapeau as a Google Summer of Code project, scikit-learn is currently maintained by volunteers..Data science practitioner Jason Brownlee from Machine Learning Mastery notes that the library focuses on modeling data but not on its loading, manipulation, and summarization..It also helps that these tools are widely adopted and have been battle tested for many years by many people.”“The AltexSoft data science team mostly uses Python libraries like scikit-learn and xgboost for classification and regression tasks,” observes Aleksander.Andrii Babii prefers to use scikit-learn with R language libraries and packages..“I’m using this combination because it open source, has very reach functions and complement each other,” explains the data scientist.NLTK is a platform for the development of Python programs to work with human language.Aleksander Konduforov prefers this tool for NLP tasks. “NLTK is a pretty much a standard library in Python for text processing which has many useful features..All these libraries are free and provide enough functionality for solving a majority of our tasks, ” the expert notes.TensorFlow is an open source software library for machine learning and deep neural network research developed and released by the Google Brain Team within Google’s AI organization in 2015.A significant feature of this library is that numerical computations are done with data flow graphs consisting of nodes and edges..Samsung Ukraine’s lead engineer Vitaliy Bulygin suggests, “If you need to implement something on Android, use TensorFlow.”Curtis Boyd, CEO of Objection Co, provider of an automated bad-review removal strategy for businesses, says his team chose to do machine learning with TensorFlow because it’s open sourced and very easy to integrate with.TensorBoard is a suite of tools for graphical representation of different aspects and stages of machine learning in TensorFlow.TensorBoard reads TensorFlow event files containing summary data (observations about a model’s specific operations) being generated while TensorFlow is running.A model structure shown with graphs allows researchers to make sure model components are located where needed and are connected correctly..For instance, it’s very comfortable to monitor how a model performs when tweaking its hyperparameters and choosing the one that performs best,” summarizes Illia.Besides displaying performance metrics, TensorBoard can show users a lot of other information like histograms, audio, text, and image data, distributions, embeddings, and scalars.PyTorch is an open source machine learning framework for deep neural networks that supports and accelerates GPUs.. 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