Programming Tools
1. Keras:
Keras is an open source high-level neural network library written in Python.
It was originally written by François Chollet in 2015 and developed by various
developers. Keras is capable of running on top of TensorFlow, Microsoft Cognitive
Toolkit or Theano. Focused on being user-friendly, modular, and extensible.
It was developed with a focus on enabling fast experimentation;
It also allows users to productize deep models on smartphones (iOS and Android),
on the web, or on the Java Virtual Machine. It offers a higher-level,
a more intuitive set of abstractions that make it easy to develop deep learning models
regardless of the computational backend use.
Version updates: https://github.com/keras-team/keras/releases
Upcoming conference: https://conferences.oreilly.com/artificial-intelligence
2.Python:
Python is widely used as a general-purpose, high-level programming language.
It was initially designed by Guido van Rossum in 1991 and developed by
Python Software Foundation. Python is a programming language that lets you
work quickly and integrate systems more efficiently.
There are two major Python versions- Python 2 and Python 3. Both are quite
different. Python is used for web development (server-side),
software development, mathematics, system scripting.
What can Python do?
Ø Python can be used on a server to create web applications.
Ø Python can be used alongside software to create workflows.
Ø Python can connect to database systems. It can also read and modify files.
Ø Python can be used to handle big data and perform complex mathematics.
Ø Python can be used for rapid prototyping, or for production-ready software
development.
Version updates: https://www.python.org/downloads/
Upcoming conference: https://www.python.org/community/workshops/
3.TensorFlow:
TensorFlow was developed by Google's Brain team for internal Google use.
It was released under the Apache 2.0 open source license on November 9, 2015.
TensorFlow is an open-source software library for dataflow programming across
a range of tasks.TensorFlow is a symbolic math library and is also used for
machine learning applications such as neural networks. It is used for both
research and production at Google. The core component of TensorFlow is the
computational graph and Tensors which traverse among all the nodes through
edges. Google uses Tensorflow for implementing Machine learning in almost
all applications. For example, if you are using Google photos or
Google voice search then you are using Tensorflow models indirectly,
they work on large clusters of Google hardware and are powerful in perceptual
tasks.
Use of TensorFlow:
Ø Deep Speech: A TensorFlow implementation motivated by Baidu's Deep
Speech architecture.
Ø RankBrain: A large-scale deployment of deep neural nets for search ranking
on www.google.com.
Ø Inception Image Classification Model: Baseline model and follow on research
into highly accurate computer vision models, starting with the model that
won the 2014 Imagenet image classification challenge.
Ø SmartReply: Deep LSTM model to automatically generate email responses.
Ø Massively Multitask Networks for Drug Discovery: A deep neural network
model for identifying promising drug candidates.
Ø On-Device Computer Vision for OCR: On-device computer vision model to do
optical character recognition to enable real-time translation.
Version updates: https://www.tensorflow.org/versions/
Upcoming conference: https://www.omicsonline.org/conferences-list/tensorflow
4.R:
R is an implementation of the S programming language combined with lexical
scoping semantics, inspired by Scheme. R is an integrated suite of software
facilities for data manipulation, calculation, and graphical display.
Among other things it has
Features:
• an effective data handling and storage facility,
• a suite of operators for calculations on arrays, in particular, matrices,
• a large, coherent, integrated collection of intermediate tools for data analysis,
• graphical facilities for data analysis and display either directly at the computer
or on hardcopy.
• a well developed, simple and effective programming language (called ‘S’)
which includes conditionals, loops, user defined recursive functions and input,
and output facilities. (Most of the system supplied functions are
themselves wrote in the S language.)
Version updates: https://cran.r-project.org/bin/windows/base/
Upcoming conference: https://jumpingrivers.github.io/meetingsR/events.html
5.Go:
Go (often referred to as Golang) The language was announced in November 2009.
Version 1.0 was released in March 2012. It is used in some of Google's production
systems, as well as by many other companies and open source projects. Go is a
statically typed, compiled language in the tradition of C, with memory safety,
garbage collection, structural typing, and CSP-style concurrency. The compiler,
tools, and source code are all free and open source.
Features:
• The go language is very concise, simple and safe.
• It’s compilation time is very fast.
• It supports the patterns which adapt to the surrounding environment similar to dynamic languages.
• It supports inbuilt concurrency such as lightweight processes channels and select statements.
• It supports interfaces and embedded types.
• It doesn’t require any external dependencies for the production of the statically linked native
binaries.
Version updates: https://golang.org/doc/devel/release.html
Upcoming conference: https://github.com/golang/go/wiki/Conferences
6.JupyterLab:
JupyterLab is the next-generation user interface for Project Jupyter.
It offers all the familiar building blocks of the classic Jupyter Notebook
(notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible
and powerful user interface. The first stable release was announced on
February 20, 2018. The Project Jupyter (formally known as IPython) is a polyglot,
web-based, open-source data science tool. Fostering reuse and reproducibility,
it supports interactive data science and scientific computing across multiple
programming languages (the so-called kernels) via the idea of notebooks.
Version updates: https://anaconda.org/conda-forge/jupyterlab
Upcoming conference: https://conferences.oreilly.com/jupyter/jup-ny
7.CoffeeScript:
On December 13, 2009, Jeremy Ashkenas made the first Git commit of
CoffeeScript with the comment: "initial commit of the mystery language.
The compiler was written in Ruby. CoffeeScript is a programming language that
transcompiles to JavaScript. It adds syntactic sugar inspired by Ruby, Python,
and Haskell in an effort to enhance JavaScript's brevity and readability.
Specific additional features include list comprehension and pattern matching.
Most modern JavaScript features that CoffeeScript supports can run natively in
Node 7.6+, meaning that Node can run CoffeeScript’s output without any further
processing required.
Usage:
Ø Command Line: Once installed, you should have access to the coffee command, which can
execute scripts, compile .coffee files into .js, and provide an interactive REPL.
Ø Transpilation: CoffeeScript 2 generates JavaScript that uses the latest, modern syntax.
Ø Transpiling with the CoffeeScript compiler: To make things easy, CoffeeScript has built-in
support for the popular Babel transpiler.
Version updates: https://github.com/jashkenas/coffeescript/releases
Upcoming conference: https://www.coffeefest.com/
8.Ruby:
Ruby is a dynamic, interpreted, reflective, object-oriented, general-purpose
programming language. It was designed and developed in the mid-1990s by
Yukihiro "Matz" Matsumoto in Japan. It supports multiple programming paradigms,
including functional, object-oriented, and imperative. It also has a dynamic
type system and automatic memory management. Ruby is designed for
programmer productivity and fun, following the principles of good user
interface design. Ruby is said to follow the principle of least astonishment (POLA),
meaning that the language should behave in such a way as to minimize
confusion for experienced users. It has been used across the board for
everything from web applications, to web servers themselves, to intelligent
graphing libraries, to picture recognition engines, to threaded database servers,
to low-level system utilities. It has a wide spectrum of use throughout computing.
Version updates: https://www.ruby-lang.org/en/downloads/releases/
Upcoming conference: https://rubyconferences.org/
Keras is an open source high-level neural network library written in Python.
It was originally written by François Chollet in 2015 and developed by various
developers. Keras is capable of running on top of TensorFlow, Microsoft Cognitive
Toolkit or Theano. Focused on being user-friendly, modular, and extensible.
It was developed with a focus on enabling fast experimentation;
It also allows users to productize deep models on smartphones (iOS and Android),
on the web, or on the Java Virtual Machine. It offers a higher-level,
a more intuitive set of abstractions that make it easy to develop deep learning models
regardless of the computational backend use.
Version updates: https://github.com/keras-team/keras/releases
Upcoming conference: https://conferences.oreilly.com/artificial-intelligence
2.Python:
Python is widely used as a general-purpose, high-level programming language.
It was initially designed by Guido van Rossum in 1991 and developed by
Python Software Foundation. Python is a programming language that lets you
work quickly and integrate systems more efficiently.
There are two major Python versions- Python 2 and Python 3. Both are quite
different. Python is used for web development (server-side),
software development, mathematics, system scripting.
What can Python do?
Ø Python can be used on a server to create web applications.
Ø Python can be used alongside software to create workflows.
Ø Python can connect to database systems. It can also read and modify files.
Ø Python can be used to handle big data and perform complex mathematics.
Ø Python can be used for rapid prototyping, or for production-ready software
development.
Version updates: https://www.python.org/downloads/
Upcoming conference: https://www.python.org/community/workshops/
3.TensorFlow:
TensorFlow was developed by Google's Brain team for internal Google use.
It was released under the Apache 2.0 open source license on November 9, 2015.
TensorFlow is an open-source software library for dataflow programming across
a range of tasks.TensorFlow is a symbolic math library and is also used for
machine learning applications such as neural networks. It is used for both
research and production at Google. The core component of TensorFlow is the
computational graph and Tensors which traverse among all the nodes through
edges. Google uses Tensorflow for implementing Machine learning in almost
all applications. For example, if you are using Google photos or
Google voice search then you are using Tensorflow models indirectly,
they work on large clusters of Google hardware and are powerful in perceptual
tasks.
Use of TensorFlow:
Ø Deep Speech: A TensorFlow implementation motivated by Baidu's Deep
Speech architecture.
Ø RankBrain: A large-scale deployment of deep neural nets for search ranking
on www.google.com.
Ø Inception Image Classification Model: Baseline model and follow on research
into highly accurate computer vision models, starting with the model that
won the 2014 Imagenet image classification challenge.
Ø SmartReply: Deep LSTM model to automatically generate email responses.
Ø Massively Multitask Networks for Drug Discovery: A deep neural network
model for identifying promising drug candidates.
Ø On-Device Computer Vision for OCR: On-device computer vision model to do
optical character recognition to enable real-time translation.
Version updates: https://www.tensorflow.org/versions/
Upcoming conference: https://www.omicsonline.org/conferences-list/tensorflow
4.R:
R is an implementation of the S programming language combined with lexical
scoping semantics, inspired by Scheme. R is an integrated suite of software
facilities for data manipulation, calculation, and graphical display.
Among other things it has
Features:
• an effective data handling and storage facility,
• a suite of operators for calculations on arrays, in particular, matrices,
• a large, coherent, integrated collection of intermediate tools for data analysis,
• graphical facilities for data analysis and display either directly at the computer
or on hardcopy.
• a well developed, simple and effective programming language (called ‘S’)
which includes conditionals, loops, user defined recursive functions and input,
and output facilities. (Most of the system supplied functions are
themselves wrote in the S language.)
Version updates: https://cran.r-project.org/bin/windows/base/
Upcoming conference: https://jumpingrivers.github.io/meetingsR/events.html
5.Go:
Go (often referred to as Golang) The language was announced in November 2009.
Version 1.0 was released in March 2012. It is used in some of Google's production
systems, as well as by many other companies and open source projects. Go is a
statically typed, compiled language in the tradition of C, with memory safety,
garbage collection, structural typing, and CSP-style concurrency. The compiler,
tools, and source code are all free and open source.
Features:
• The go language is very concise, simple and safe.
• It’s compilation time is very fast.
• It supports the patterns which adapt to the surrounding environment similar to dynamic languages.
• It supports inbuilt concurrency such as lightweight processes channels and select statements.
• It supports interfaces and embedded types.
• It doesn’t require any external dependencies for the production of the statically linked native
binaries.
Version updates: https://golang.org/doc/devel/release.html
Upcoming conference: https://github.com/golang/go/wiki/Conferences
6.JupyterLab:
JupyterLab is the next-generation user interface for Project Jupyter.
It offers all the familiar building blocks of the classic Jupyter Notebook
(notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible
and powerful user interface. The first stable release was announced on
February 20, 2018. The Project Jupyter (formally known as IPython) is a polyglot,
web-based, open-source data science tool. Fostering reuse and reproducibility,
it supports interactive data science and scientific computing across multiple
programming languages (the so-called kernels) via the idea of notebooks.
Version updates: https://anaconda.org/conda-forge/jupyterlab
Upcoming conference: https://conferences.oreilly.com/jupyter/jup-ny
7.CoffeeScript:
On December 13, 2009, Jeremy Ashkenas made the first Git commit of
CoffeeScript with the comment: "initial commit of the mystery language.
The compiler was written in Ruby. CoffeeScript is a programming language that
transcompiles to JavaScript. It adds syntactic sugar inspired by Ruby, Python,
and Haskell in an effort to enhance JavaScript's brevity and readability.
Specific additional features include list comprehension and pattern matching.
Most modern JavaScript features that CoffeeScript supports can run natively in
Node 7.6+, meaning that Node can run CoffeeScript’s output without any further
processing required.
Usage:
Ø Command Line: Once installed, you should have access to the coffee command, which can
execute scripts, compile .coffee files into .js, and provide an interactive REPL.
Ø Transpilation: CoffeeScript 2 generates JavaScript that uses the latest, modern syntax.
Ø Transpiling with the CoffeeScript compiler: To make things easy, CoffeeScript has built-in
support for the popular Babel transpiler.
Version updates: https://github.com/jashkenas/coffeescript/releases
Upcoming conference: https://www.coffeefest.com/
8.Ruby:
Ruby is a dynamic, interpreted, reflective, object-oriented, general-purpose
programming language. It was designed and developed in the mid-1990s by
Yukihiro "Matz" Matsumoto in Japan. It supports multiple programming paradigms,
including functional, object-oriented, and imperative. It also has a dynamic
type system and automatic memory management. Ruby is designed for
programmer productivity and fun, following the principles of good user
interface design. Ruby is said to follow the principle of least astonishment (POLA),
meaning that the language should behave in such a way as to minimize
confusion for experienced users. It has been used across the board for
everything from web applications, to web servers themselves, to intelligent
graphing libraries, to picture recognition engines, to threaded database servers,
to low-level system utilities. It has a wide spectrum of use throughout computing.
Version updates: https://www.ruby-lang.org/en/downloads/releases/
Upcoming conference: https://rubyconferences.org/
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