Difference between revisions of "Libraries & Frameworks"

Latest revision as of 11:51, 6 November 2024

YouTube ... Quora ...Google search ...Google News ...Bing News

Libraries & Frameworks Overview ... Libraries & Frameworks ... Git - GitHub and GitLab ... Other Coding options
Development ... Notebooks ... AI Pair Programming ... Codeless ... Hugging Face ... AIOps/MLOps ... AIaaS/MLaaS
Python ... GenAI w/ Python ... JavaScript ... GenAI w/ JavaScript ... TensorFlow ... PyTorch
GPU
Gaming ... Game-Based Learning (GBL) ... Security ... Generative AI ... Games - Metaverse ... Quantum ... Game Theory ... Design
Machine Translation open-source toolkits | Tsinghua Natural Language Processing Group
Machine Learning Software | DMOZtools.net

A library and a framework are both code written by someone else that help you solve common problems in easier ways. However, they differ in how they control the flow of your application.

A library is a collection of functions or classes that you can call from your own code to perform specific tasks. For example, if you want to manipulate strings, you can use a library that provides string functions. You are in charge of when and where to use the library functions. Some examples of AI-related libraries are TensorFlow, Theano, and PyTorch.
A framework is a set of rules or guidelines that define the structure and behavior of your application. For example, if you want to build a web application, you can use a framework that provides templates, routing, authentication, etc. The framework calls your code at certain points, following the inversion of control principle. Some examples of AI-related frameworks are Angular, Vue, and Microsoft CNTK.

The main difference between a library and a framework is who is in control: you control the library, but the framework controls you.

With pages at Primo.ai or not included in Wikipedia Deep learning comparison chart below

scikit-learn
PyTorch
ConvNetJS | Andrej Karpathy
Accord.Net Framework
Caffe / Caffe2
Microsoft Cognitive Toolkit - was Cognitive Toolkit (CNTK)
gluon
H20
MXNet
Neuroph
neon
Deeplearning4j
theano
Spark MLlib
Cloudera Oryx
GoLearn
Weka
Apache Mahout
Shogun
Ray - UC Berkeley RISELab
MLPACK (C++ library)
Accord.NET
OpenCV Open Computer Vision - work with images and/or videos and wish to add a variety of classical and state-of-the-art vision algorithms to their toolbox.
OpenCog, a GPL-licensed framework for artificial intelligence written in C++, Python and Scheme.
RapidMiner, an environment for machine learning and data mining, now developed commercially.
Weka, a free implementation of many machine learning algorithms in Java.

Jeremy Howard: Deep Learning Frameworks - TensorFlow, PyTorch, fast.ai \| AI Podcast Clips This is a clip from a conversation with Jeremy Howard from Aug 2019. New full episodes every Mon & Thu and 1-2 new clips or a new non-podcast video on all other days.

Deep Learning Frameworks 2019 Siraj Raval Which deep learning framework should you use? In this video I'll compare 10 deep learning frameworks across a wide variety of metrics. PyTorch, TensorFlow, MXNet, Chainer, CNTK, Sonnet, DeepLearning4J, CoreML, ONNX, we've got a lot to cover in this video! Using code, programmatic features, and theory, I'll navigate this field ultimately coming to some clear conclusions. Enjoy!

Deep learning software by name

Software	Creator	Software licenseTemplate:Efn	Open source	Platform	Written in	Interface	OpenMP support	OpenCL support	CUDA support	Parallel execution (multi node)	Automatic differentiation<ref>Template:Cite arXiv</ref>	Has pretrained models	Recurrent nets	Convolutional nets	RBM/DBNs	Metal support
roNNie.ai	Kevin Lok	MIT	Template:Yes	Linux, macOS, Windows	Python	Python			Template:Yes			Template:Yes	Template:Yes	Template:Yes
BigDL	Jason Dai	Apache 2.0	Yes	Apache Spark	Scala	Scala, Python			Template:No			Template:Yes	Template:Yes	Template:Yes
Caffe / Caffe2	Berkeley Vision and Learning Center	Template:Free	Template:Yes	Linux, macOS, Windows<ref>Template:Cite web</ref>	C++	Python, MATLAB, C++	Template:Yes	Template:Depends	Template:Yes	Template:Yes	Template:Yes<ref>Template:Cite web</ref>	Template:Yes	Template:Yes	Template:No	Template:Dunno
Deeplearning4j	Skymind engineering team; Deeplearning4j community; originally Adam Gibson	Template:Free	Template:Yes	Linux, macOS, Windows, Android (Cross-platform)	C++, Java	Java, Scala, Clojure, Python (Keras), Kotlin	Template:Yes	Template:Depends<ref>Template:Cite web</ref>	Template:Yes<ref>Template:Cite web</ref><ref>Template:Cite web</ref>	Template:Yes	Template:Yes<ref>Template:Cite web</ref>	Template:Yes	Template:Yes	Template:Yes	Template:Yes<ref>Template:Cite web</ref>
Chainer	Preferred Networks	Template:Free	Template:Yes	Linux, macOS, Windows		Python	Template:No	Template:No<ref>https://github.com/chainer/chainer/pull/2717</ref><ref>https://github.com/chainer/chainer/issues/99</ref>	Template:Yes	Template:Yes	Template:Yes	Template:Yes	Template:Yes
Darknet	Joseph Redmon	Template:Free	Template:Yes	Cross-Platform	C	C, Python	Template:Yes	Template:No<ref>https://github.com/pjreddie/darknet/issues/127</ref>	Template:Yes	Template:Yes
Dlib	Davis King	Template:Free	Template:Yes	Cross-Platform	C++	C++	Template:Yes	Template:No	Template:Yes	Template:Yes	Template:Yes	Template:Yes	Template:Yes	Template:Yes	Template:Yes
DataMelt (DMelt)	S.Chekanov	Template:Free	Template:Yes	Cross-Platform	Java	Java	Template:No	Template:No	Template:No	Template:No	Template:No	Template:No	Template:No	Template:No	Template:No
DyNet	Carnegie Mellon University	Template:Free	Template:Yes	Linux, macOS, Windows		C++, Python		Template:No<ref>https://github.com/clab/dynet/issues/405</ref>	Template:Yes	Template:Yes	Template:Yes
Intel Data Analytics Acceleration Library	Intel	Template:Free	Template:Yes	Linux, macOS, Windows on Intel CPU<ref name="intel-daal">Intel® Data Analytics Acceleration Library (Intel® DAAL) \| Intel® Software</ref>	C++, Python, Java	C++, Python, Java<ref name="intel-daal"/>	Template:Yes	Template:No	Template:No	Template:Yes	Template:No		Template:Yes		Template:Yes
Intel Math Kernel Library	Intel	Template:Proprietary	Template:No	Linux, macOS, Windows on Intel CPU<ref>Intel® Math Kernel Library (Intel® MKL) \| Intel® Software</ref>		C<ref>Deep Neural Network Functions</ref>	Template:Yes<ref>Using Intel® MKL with Threaded Applications \| Intel® Software</ref>	Template:No	Template:No	Template:Yes	Template:No	Template:Yes<ref name="intel-benchmark">Intel® Xeon Phi™ Delivers Competitive Performance For Deep Learning—And Getting Better Fast \| Intel® Software</ref>	Template:Yes<ref name="intel-benchmark"/>		Template:No
Keras	François Chollet	Template:Free	Template:Yes	Linux, macOS, Windows	Python	Python, R	Template:Depends	Template:Depends	Template:Yes	Template:Yes	Template:Yes<ref>https://keras.io/applications/</ref>	Template:Yes	Template:Yes	Template:Yes	Template:Yes<ref>Does Keras support using multiple GPUs? · Issue #2436 · fchollet/keras</ref>
MATLAB + Neural Network Toolbox	MathWorks	Template:Proprietary	Template:No	Linux, macOS, Windows	C, C++, Java, MATLAB	MATLAB	Template:No	Template:No	Template:Yes<ref>Template:Cite web</ref>	Template:No	Template:Yes<ref name="NNT">Template:Cite web</ref><ref>Template:Cite web</ref>	Template:Yes<ref name="NNT"/>	Template:Yes<ref name="NNT"/>	Template:Yes<ref name="NNT"/>	Template:Yes<ref>Template:Cite web</ref>
Microsoft Cognitive Toolkit	Microsoft Research	Template:Free<ref>Template:Cite web</ref>	Template:Yes	Windows, Linux<ref name="Setup CNTK on your machine">Template:Cite web</ref> (macOS via Docker on roadmap)	C++	Python (Keras), C++, Command line,<ref>Template:Cite web</ref> BrainScript<ref>Template:Cite web</ref> (.NET on roadmap<ref>Template:Cite web</ref>)	Template:Yes<ref>Template:Cite web</ref>	Template:No	Template:Yes	Template:Yes	Template:Yes<ref>https://github.com/Microsoft/CNTK/issues/140#issuecomment-186466820</ref>	Template:Yes<ref name="cntk.ai">Template:Cite web</ref>	Template:Yes<ref name="cntk.ai" />	Template:Yes	Template:Yes<ref>Template:Cite web</ref>
Apache MXNet	Apache Software Foundation	Template:Free	Template:Yes	Linux, macOS, Windows,<ref>Template:Cite web</ref><ref>Template:Cite web</ref> AWS, Android,<ref>Template:Cite web</ref> iOS, JavaScript<ref>Template:Cite web</ref>	Small C++ core library	C++, Python, Julia, Matlab, JavaScript, Go, R, Scala, Perl	Template:Yes	Template:Depends<ref>Template:Cite web</ref>	Template:Yes	Template:Yes<ref>https://mxnet.readthedocs.io/</ref>	Template:Yes<ref>Template:Cite web</ref>	Template:Yes	Template:Yes	Template:Yes	Template:Yes<ref>Template:Cite web</ref>
Neural Designer	Artelnics	Template:Proprietary	Template:No	Linux, macOS, Windows	C++	Graphical user interface	Template:Yes	Template:No	Template:No	Template:Dunno	Template:Dunno	Template:No	Template:No	Template:No	Template:Dunno
OpenNN	Artelnics	Template:Free	Template:Yes	Cross-platform	C++	C++	Template:Yes	Template:No	Template:Yes	Template:Dunno	Template:Dunno	Template:No	Template:No	Template:No	Template:Dunno
PlaidML	Vertex.AI	AGPL3	Template:Yes	Linux, macOS, Windows	C++, Python	Keras, Python, C++, C	Template:No	Template:Yes	Template:Yes	Template:Yes	Template:Yes		Template:Yes	Template:Yes	Template:Dunno	Template:Yes
PyTorch	Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan	Template:Free	Template:Yes	Linux, macOS, Windows	Python, C, CUDA	Python	Template:Yes	Template:Depends<ref>https://github.com/hughperkins/pytorch-coriander</ref><ref>https://github.com/pytorch/pytorch/issues/488</ref><ref>https://github.com/pytorch/pytorch/issues/488#issuecomment-273626736</ref>	Template:Yes	Template:Yes	Template:Yes	Template:Yes	Template:Yes		Template:Yes
Apache SINGA	Apache Incubator	Template:Free	Template:Yes	Linux, macOS, Windows	C++	Python, C++, Java	Template:No	Template:No	Template:Yes	Template:Dunno	Template:Yes	Template:Yes	Template:Yes	Template:Yes	Template:Yes
TensorFlow	Google Brain team	Template:Free	Template:Yes	Linux, macOS, Windows,<ref>https://developers.googleblog.com/2016/11/tensorflow-0-12-adds-support-for-windows.html</ref> Android	C++, Python, CUDA	Python (Keras), C/C++, Java, Go, R<ref>Template:Citation</ref>, Julia	Template:No	Template:Depends<ref name="tensorflow-roadmap">Template:Cite web</ref> but already with SYCL<ref name="GitHub">Template:Cite web</ref> support	Template:Yes	Template:Yes<ref>https://www.tensorflow.org/</ref>	Template:Yes<ref>https://github.com/tensorflow/models</ref>	Template:Yes	Template:Yes	Template:Yes	Template:Yes
TensorLayer	Hao Dong	Template:Free	Template:Yes	Linux, macOS, Windows,<ref>https://developers.googleblog.com/2016/11/tensorflow-0-12-adds-support-for-windows.html</ref> Android	C++, Python,	Python	Template:No	Template:Depends<ref name="tensorflow-roadmap">Template:Cite web</ref> but already with SYCL<ref name="GitHub">Template:Cite web</ref> support	Template:Yes	Template:Yes<ref>https://www.tensorflow.org/</ref>	Template:Yes<ref>https://github.com/tensorflow/models</ref>	Template:Yes	Template:Yes	Template:Yes	Template:Yes
Theano	Université de Montréal	Template:Free	Template:Yes	Cross-platform	Python	Python (Keras)	Template:Yes	Template:Depends	Template:Yes	Template:Yes<ref>https://deeplearning.net/software/theano/library/gradient.html</ref><ref>https://groups.google.com/d/msg/theano-users/mln5g2IuBSU/gespG36Lf_QJ</ref>	Template:Depends	Template:Yes	Template:Yes	Template:Yes	Template:Yes<ref>Using multiple GPUs — Theano 0.8.2 documentation</ref>
Torch	Ronan Collobert, Koray Kavukcuoglu, Clement Farabet	Template:Free	Template:Yes	Linux, macOS, Windows,<ref>https://github.com/torch/torch7/wiki/Windows</ref> Android,<ref>Template:Cite web</ref> iOS	C, Lua	Lua, LuaJIT,<ref>Template:Cite web</ref> C, utility library for C++/OpenCL<ref name=jtorch>Template:Cite web</ref>	Template:Yes	Template:Depends	Template:Yes<ref>Template:Cite web</ref><ref>Template:Cite web</ref>	Template:Yes	Template:Yes<ref>Template:Cite web</ref>	Template:Yes	Template:Yes	Template:Yes	Template:Yes<ref>https://github.com/torch/torch7/wiki/Cheatsheet#distributed-computing--parallel-processing</ref>
Wolfram Mathematica	Wolfram Research	Template:Proprietary	Template:No	Windows, macOS, Linux, Cloud computing	C++, Wolfram Language, CUDA	Wolfram Language	Template:Yes	Template:No	Template:Yes	Template:Yes	Template:Yes<ref>https://resources.wolframcloud.com/NeuralNetRepository</ref>	Template:Yes	Template:Yes	Template:Yes	Template:Depends
VerAI	VerAI	Template:Proprietary	Template:No	Linux, Web-based	C++,Python, Go, Angular	Graphical user interface, cli	Template:No	Template:No	Template:Yes	Template:Yes	Template:Yes	Template:Yes	Template:Yes	Template:Yes	Template:Yes

@@ Line 2: / Line 2: @@
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-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
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-|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
+<!-- Google tag (gtag.js) -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+  gtag('config', 'G-4GCWLBVJ7T');
+</script>
 }}
-[http://www.youtube.com/results?search_query=library+framework+deep+learning+artificial+intelligence+deep+learning Youtube search...]
+[https://www.youtube.com/results?search_query=ai+library+Libraries+framework YouTube]
-[http://www.google.com/search?q=library+framework+deep+machine+learning+deep+learning+ML+artificial+intelligence ...Google search]
+[https://www.quora.com/search?q=ai%20library%20Libraries%20framework ... Quora]
+[https://www.google.com/search?q=ai+library+Libraries+framework ...Google search]
+[https://news.google.com/search?q=ai+library+Libraries+framework ...Google News]
+[https://www.bing.com/news/search?q=ai+library+Libraries+framework&qft=interval%3d%228%22 ...Bing News]
+* [[Libraries & Frameworks Overview]] ... [[Libraries & Frameworks]] ... [[Git - GitHub and GitLab]] ... [[Other Coding options]]
+* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+* [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]]
+* [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU|GPU]]
+* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
+* [https://github.com/THUNLP-MT Machine Translation open-source toolkits | Tsinghua Natural Language Processing Group]
+* [https://www.dmoztools.net/Computers/Artificial_Intelligence/Machine_Learning/Software/ Machine Learning Software | DMOZtools.net]
+A library and a framework are both code written by someone else that help you solve common problems in easier ways. However, they differ in how they control the flow of your application.
+* <b>A library</b> is a collection of functions or classes that you can call from your own code to perform specific tasks. For example, if you want to manipulate strings, you can use a library that provides string functions. You are in charge of when and where to use the library functions. Some examples of AI-related libraries are TensorFlow, Theano, and PyTorch.
+* <b>A framework</b> is a set of rules or guidelines that define the structure and behavior of your application. For example, if you want to build a web application, you can use a framework that provides templates, routing, authentication, etc. The framework calls your code at certain points, following the inversion of control principle. Some examples of AI-related frameworks are Angular, Vue, and Microsoft CNTK.
+The main difference between a library and a framework is who is in control: you control the library, but the framework controls you.
-* [[Libraries & Frameworks Overview]]
-* [[Platforms: Machine Learning as a Service (MLaaS)]]
-* [http://github.com/THUNLP-MT Machine Translation open-source toolkits | Tsinghua Natural Language Processing Group]
-* [http://www.dmoztools.net/Computers/Artificial_Intelligence/Machine_Learning/Software/ Machine Learning Software | DMOZtools.net]
 === With pages at Primo.ai or not included in Wikipedia  Deep learning comparison chart below ===
@@ Line 17: / Line 40: @@
 * [[Python#scikit-learn|scikit-learn]]
 * [[PyTorch]]
-* [[ConvNetJS]]
+* [[ConvNetJS]] | [[Creatives#Andrej Karpathy |Andrej Karpathy]]
 * [[Accord.Net Framework]]
 * [[Caffe / Caffe2]]
@@ Line 29: / Line 52: @@
 * [[theano]]
 * [[Spark MLlib]]
-* [[Cloudera Oryx]]
+* [[Cloudera]] Oryx
 * [[GoLearn]]
 * [[Weka]]
 * [[Apache Mahout]]
 * [[Shogun]]
-* [http://en.wikipedia.org/wiki/MLPACK_(C%2B%2B_library) MLPACK (C++ library)]
+* [[Ray - UC Berkeley RISELab]]
-* [http://en.wikipedia.org/wiki/Accord.NET Accord.NET]
+* [https://en.wikipedia.org/wiki/MLPACK_(C%2B%2B_library) MLPACK (C++ library)]
-* [http://opencv.org/ OpenCV (Open Source Computer Vision Library)]
+* [https://en.wikipedia.org/wiki/Accord.NET Accord.NET]
-* [http://en.wikipedia.org/wiki/OpenCog  OpenCog], a GPL-licensed framework for artificial intelligence written in C++, Python and Scheme.
+* [[Python#OpenCV| OpenCV]] Open Computer Vision - work with images and/or videos and wish to add a variety of classical and state-of-the-art vision algorithms to their toolbox.
-* [http://en.wikipedia.org/wiki/RapidMiner  RapidMiner], an environment for machine learning and [[data mining]], now developed commercially.
+* [https://en.wikipedia.org/wiki/OpenCog  OpenCog], a GPL-licensed framework for artificial intelligence written in C++, Python and Scheme.
-* [http://en.wikipedia.org/wiki/Weka  Weka], a free implementation of many machine learning algorithms in Java.
+* [https://en.wikipedia.org/wiki/RapidMiner  RapidMiner], an environment for machine learning and [[data mining]], now developed commercially.
+* [https://en.wikipedia.org/wiki/Weka  Weka], a free implementation of many machine learning algorithms in Java.
+{|<!-- T -->
+| valign="top" |
+{| class="wikitable" style="width: 550px;"
+||
+<youtube>XHyASP49ses</youtube>
+<b>Jeremy Howard: Deep Learning Frameworks - TensorFlow, PyTorch, fast.ai | AI Podcast Clips
+</b><br>This is a clip from a conversation with Jeremy Howard from Aug 2019. New full episodes every Mon & Thu and 1-2 new clips or a new non-podcast video on all other days.
+|}
+|<!-- M -->
+| valign="top" |
+{| class="wikitable" style="width: 550px;"
+||
+<youtube>SJldOOs4vB8</youtube>
+<b>Deep Learning Frameworks 2019
+</b><br>[[Creatives#Siraj Raval|Siraj Raval]]  Which deep learning framework should you use? In this video I'll compare 10 deep learning frameworks across a wide variety of metrics. [[PyTorch]], [[TensorFlow]], MXNet, Chainer, CNTK, Sonnet, DeepLearning4J, CoreML, ONNX, we've got a lot to cover in this video! Using code, programmatic features, and theory, I'll navigate this field ultimately coming to some clear conclusions. Enjoy!
+|}
+|}<!-- B -->
 == Deep learning software by name==
@@ Line 109: / Line 151: @@
 | {{Yes}}
 | {{Yes}}
-| {{Yes}}<ref>{{cite web|url=http://caffe.berkeleyvision.org/model_zoo.html|title=Caffe Model Zoo}}</ref>
+| {{Yes}}<ref>{{cite web|url=https://caffe.berkeleyvision.org/model_zoo.html|title=Caffe Model Zoo}}</ref>
 | {{Yes}}
 | {{Yes}}
@@ Line 125: / Line 167: @@
 | {{Yes}}
 | {{Depends|On roadmap}}<ref>{{cite web|url=https://github.com/deeplearning4j/nd4j/issues/27|title=Support for Open CL · Issue #27 · deeplearning4j/nd4j|work=GitHub}}</ref>
-| {{Yes}}<ref>{{cite web|url=http://nd4j.org/gpu_native_backends.html|title=N-Dimensional Scientific Computing for Java|publisher=}}</ref><ref>{{cite web|url=https://deeplearning4j.org/compare-dl4j-tensorflow-pytorch|title=Comparing Top Deep Learning Frameworks|publisher=Deeplearning4j}}</ref>
+| {{Yes}}<ref>{{cite web|url=https://nd4j.org/gpu_native_backends.html|title=N-Dimensional Scientific Computing for Java|publisher=}}</ref><ref>{{cite web|url=https://deeplearning4j.org/compare-dl4j-tensorflow-pytorch|title=Comparing Top Deep Learning Frameworks|publisher=Deeplearning4j}}</ref>
 | {{Yes|Computational Graph}}
-| {{Yes}}<ref>{{cite web|url=http://deeplearning4j.org/model-zoo|title=Deeplearning4j Models|author1=Chris Nicholson|author2= Adam Gibson|publisher=}}</ref>
+| {{Yes}}<ref>{{cite web|url=https://deeplearning4j.org/model-zoo|title=Deeplearning4j Models|author1=Chris Nicholson|author2= Adam Gibson|publisher=}}</ref>
 | {{Yes}}
 | {{Yes}}
 | {{Yes}}
-| {{Yes}}<ref>{{cite web|url=http://deeplearning4j.org/spark|title=Deeplearning4j on Spark|author=Deeplearning4j|publisher=Deeplearning4j}}</ref>
+| {{Yes}}<ref>{{cite web|url=https://deeplearning4j.org/spark|title=Deeplearning4j on Spark|author=Deeplearning4j|publisher=Deeplearning4j}}</ref>
 |
 |-
@@ Line 308: / Line 350: @@
 | {{Yes}}
 | {{Yes}}<ref>https://github.com/Microsoft/CNTK/issues/140#issuecomment-186466820</ref>
-| {{Yes}}<ref name="cntk.ai">{{cite web|url=http://www.cntk.ai/|title=CNTK - Computational Network Toolkit|publisher=Microsoft Corporation}}</ref>
+| {{Yes}}<ref name="cntk.ai">{{cite web|url=https://www.cntk.ai/|title=CNTK - Computational Network Toolkit|publisher=Microsoft Corporation}}</ref>
 | {{Yes}}<ref name="cntk.ai" />
 | {{Yes}}
@@ Line 369: / Line 411: @@
 |-
 |[https://github.com/plaidml/plaidml PlaidML]
-|[http://vertex.ai Vertex.AI]
+|[https://vertex.ai Vertex.AI]
 |[[AGPL 3|AGPL3]]
 | {{Yes}}
@@ Line 466: / Line 508: @@
 | [[Python (programming language)|Python]] ([[Keras]])
 | {{Yes}}
-| {{Depends|Under development<ref>{{cite web|url=http://deeplearning.net/software/theano/tutorial/using_gpu.html|title=Using the GPU — Theano 0.8.2 documentation|publisher=}}</ref>}}
+| {{Depends|Under development<ref>{{cite web|url=https://deeplearning.net/software/theano/tutorial/using_gpu.html|title=Using the GPU — Theano 0.8.2 documentation|publisher=}}</ref>}}
 | {{Yes}}
-| {{Yes}}<ref>http://deeplearning.net/software/theano/library/gradient.html</ref><ref>https://groups.google.com/d/msg/theano-users/mln5g2IuBSU/gespG36Lf_QJ</ref>
+| {{Yes}}<ref>https://deeplearning.net/software/theano/library/gradient.html</ref><ref>https://groups.google.com/d/msg/theano-users/mln5g2IuBSU/gespG36Lf_QJ</ref>
 | {{Depends|Through Lasagne's model zoo<ref>{{cite web|url=https://github.com/Lasagne/Recipes/tree/master/modelzoo|title=Recipes/modelzoo at master · Lasagne/Recipes · GitHub|work=GitHub}}</ref>}}
 | {{Yes}}
 | {{Yes}}
 | {{Yes}}
-| {{Yes}}<ref>[http://deeplearning.net/software/theano/tutorial/using_multi_gpu.html Using multiple GPUs — Theano 0.8.2 documentation]</ref>
+| {{Yes}}<ref>[https://deeplearning.net/software/theano/tutorial/using_multi_gpu.html Using multiple GPUs — Theano 0.8.2 documentation]</ref>
 |
 |-
@@ Line 482: / Line 524: @@
 | [[Linux]], [[macOS]], [[Microsoft Windows|Windows]],<ref>https://github.com/torch/torch7/wiki/Windows</ref> [[Android (operating system)|Android]],<ref>{{cite web|url=https://github.com/soumith/torch-android|title=GitHub - soumith/torch-android: Torch-7 for Android|work=GitHub}}</ref> [[iOS]]
 | [[C (programming language)|C]], [[Lua (programming language)|Lua]]
-| [[Lua (programming language)|Lua]], [[Lua (programming language)|LuaJIT]],<ref>{{cite web|url=http://ronan.collobert.com/pub/matos/2011_torch7_nipsw.pdf|title=Torch7: A Matlab-like Environment for Machine Learning}}</ref> [[C (programming language)|C]], utility library for [[C++]]/[[OpenCL]]<ref name=jtorch>{{cite web|url=https://github.com/jonathantompson/jtorch|title=GitHub - jonathantompson/jtorch: An OpenCL Torch Utility Library|work=GitHub}}</ref>
+| [[Lua (programming language)|Lua]], [[Lua (programming language)|LuaJIT]],<ref>{{cite web|url=https://ronan.collobert.com/pub/matos/2011_torch7_nipsw.pdf|title=Torch7: A Matlab-like Environment for Machine Learning}}</ref> [[C (programming language)|C]], utility library for [[C++]]/[[OpenCL]]<ref name=jtorch>{{cite web|url=https://github.com/jonathantompson/jtorch|title=GitHub - jonathantompson/jtorch: An OpenCL Torch Utility Library|work=GitHub}}</ref>
 | {{Yes}}
 | {{Depends|Third party implementations<ref>{{cite web|url=https://github.com/torch/torch7/wiki/Cheatsheet#opencl|title=Cheatsheet|work=GitHub}}</ref><ref>{{cite web|url=https://github.com/hughperkins/distro-cl|title=cltorch|work=GitHub}}</ref>}}
@@ Line 505: / Line 547: @@
 | {{Yes}}
 | {{Yes}}
-| {{Yes}}<ref>http://resources.wolframcloud.com/NeuralNetRepository</ref>
+| {{Yes}}<ref>https://resources.wolframcloud.com/NeuralNetRepository</ref>
 | {{Yes}}
 | {{Yes}}

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