{"id":151391,"date":"2019-03-07T11:49:23","date_gmt":"2019-03-07T06:19:23","guid":{"rendered":"https:\/\/staging-imarticus.kinsta.cloud\/?p=151391"},"modified":"2021-09-20T05:13:44","modified_gmt":"2021-09-20T05:13:44","slug":"how-do-you-start-applying-deep-learning-for-my-problems-machine-learning-blog","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/how-do-you-start-applying-deep-learning-for-my-problems-machine-learning-blog\/","title":{"rendered":"How Do You Start Applying Deep Learning For My Problems?"},"content":{"rendered":"

Deep Learning helps machine learn by example via modern architectures like Neural Networks. A deep algorithm processes the input data using multiple linear or non-linear transformations before generating the output.
\nAs the concept and applications of Deep Learning are becoming popular, many frameworks have been designed to facilitate the modeling process. Students going for Deep Learning, Machine Learning course<\/a><\/strong> in India often face the challenge of choosing a suitable framework.
\n\"Machine<\/a>
\nFollowing list aims to help students understand the available frameworks in-order to make an informed choice about, which Deep Learning course they want to take.<\/strong>
\n1. \u00a0\u00a0\u00a0TensorFlow\u00a0<\/strong>
\nTensorFlow by Google is considered to be the best Deep Learning framework, especially for beginners. TensorFlow offers a flexible architecture that enabled many tech giants to embrace it on a scale; for example Airbus, Twitter, IBM, etc. It supports Python, C++, and R to create models and libraries. A Tensor Board is used for visualization of network modeling and performance. While for rapid development and deployment of new algorithms, Google offers TensorFlow which retains the same server architecture and APIs.
\n2.\u00a0\u00a0\u00a0 Caffe\u00a0<\/strong>
\nSupported with interfaces like C, C++, Python, MATLAB, in addition to the Command Line Interface, Caffe is famous for its speed. The biggest perk of Caffe comes with its C++ libraries that allow access to the \u2018Caffe Model Zoo\u2019, a repository containing pre-trained and ready to use networks of almost every kind. Companies like Facebook and Pinterest use Caffe for maximum performance. Caffe is very efficient when it comes to computer vision and image processing, but it is not an attractive choice for sequence modeling and Recurrent Neural Networks (RNN).
\n3.\u00a0\u00a0\u00a0 The Microsoft Cognitive Toolkit\/CNTK<\/strong>
\nMicrosoft offers Cognitive Toolkit (CNTK) an open source Deep Learning framework for creating and training Deep Learning models. CNTK specializes in creating efficient RNN and Convoluted Neural Networks (CNN) alongside image, speech, and text-based data training. It is also supported by interfaces like Python, C++ and the Command Line Interface just like Caffe. However, CNTK\u2019s capability on mobile is limited due to lack of support on ARM architecture.
\n4.\u00a0\u00a0\u00a0 Torch\/PyTorch<\/strong>
\nFacebook, Twitter and Google etc have actively adopted a Lua based Deep Learning framework PyTorch. PyTorch employs CUDA along with C\/C++ libraries for processing. The entire deep modeling process is simpler and transparent given PyTorch framework’s architectural style and its support for Python.
\n5.\u00a0\u00a0\u00a0 MXNet<\/strong>
\nMXNet is a Deep Learning framework supported by Python, R, C++, Julia, and Scala. This allows users to train their Deep Learning models with a variety of common Machine Learning languages. Along with RNN and CNN, it also supports Long Short-Term Memory (LTSM) networks. MXNet is a scalable framework making it valuable to enterprises like Amazon, which uses MXNet as its reference library for Deep Learning.
\n6.\u00a0\u00a0\u00a0 Chainer<\/strong>
\nDesigned on \u201cThe define by run\u201d strategy Chainer is a very powerful and dynamic Python based Deep Learning<\/strong><\/a> framework in use today. Supporting both CUDA and multi GPU computation, Chainer is used primarily for sentiment analysis speech recognition etc. using RNN and CNN.
\n7.\u00a0\u00a0\u00a0 Keras<\/strong>
\nKeras is a minimalist neural network library, which is lightweight and very easy to use while stocking multiple layers to build Deep Learning models. Keras was designed for quick experimentation of models to be run on TensorFlow or Theano. It is primarily used for classification, tagging, text generation and summarization, speech recognition, etc.
\n8.\u00a0\u00a0\u00a0 Deeplearning4j<\/strong>
\nDeveloped in Java and Scala, Deeplearning4j provides parallel training, micro-service architecture adaption, along with distributed CPUs and GPUs. It uses map reduce to train the network like CNN, RNN, Recursive Neural Tensor Network (RNTN) and LTSM.
\nThere are many Deep Learning, Machine Learning courses in India<\/a><\/strong> offering training on a variety of frameworks. For beginners, a Python-based framework like TensorFlow or Chainer would be more appropriate. For seasoned programmers, Java and C++ based frameworks would provide better choices for micro-management.<\/p>\n","protected":false},"excerpt":{"rendered":"

Deep Learning helps machine learn by example via modern architectures like Neural Networks. A deep algorithm processes the input data using multiple linear or non-linear transformations before generating the output. As the concept and applications of Deep Learning are becoming popular, many frameworks have been designed to facilitate the modeling process. Students going for Deep […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","_lmt_disableupdate":"no","_lmt_disable":"","footnotes":""},"categories":[23],"tags":[558,646,680,812,932,1036,1092],"class_list":["post-151391","post","type-post","status-publish","format-standard","hentry","category-analytics","tag-machine-learning","tag-artificial-intelligence","tag-artificial-intelligence-course","tag-machine-learning-course","tag-deep-learning","tag-python-course","tag-deep-learning-course"],"acf":[],"aioseo_notices":[],"modified_by":"Imarticus Learning","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/151391","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/comments?post=151391"}],"version-history":[{"count":0,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/151391\/revisions"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=151391"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=151391"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=151391"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}