There are plenty of them for example http://deeplearning.net/. If you want something like a textbook:http://www.iro.umontreal.ca/~bengioy/dlbook/version-07-08-2015/dlbook.html. If you just want to work out some problems using toolboxes. Try Theano, Caffe, Tensorflow, MXnet, etc. 

There are plenty of them for example http://deeplearning.net/. If you want something like a textbook:http://www.iro.umontreal.ca/~bengioy/dlbook/version-07-08-2015/dlbook.html. If you just want to work out some problems using toolboxes. Try Theano, Caffe, Tensorflow, MXnet, etc. 

Hi 

You can use H2O Deep learning package most useful. I used this package

http://h2o.ai/product/deep-learning/

Dear Abhishek Maity,

You can find the commonly used tools on NVidia's homepage. I prefer Torch and Keras. 

Best Regards,

Balint

https://developer.nvidia.com/deep-learning-getting-started

Thanks Heng, Kazi, Joachim, Anil, Balint

You can use this link:

https://devblogs.nvidia.com/parallelforall/deep-learning-for-computer-vision-with-matlab-and-cudnn/

http://www.deeplearningbook.org/

State-of-the-art Deep Learning publications, frameworks & resources available at my GitHub repo Adventures in deep learning

https://github.com/GKalliatakis/Adventures-in-deep-learning

https://www.slideshare.net/mohamedloey/deep-learning-71352250

Please read this:

Conference Paper HDLTex: Hierarchical Deep Learning for Text Classification

If you do not like to collect bits and  pieces of scattered information, and want to get some hand-on experience during the process, then this is the link for you: https://www.coursera.org/specializations/deep-learning

Fantastic Andrew Ng broke down this topic into easily digestible portions, just for beginners.

Try this resource.

IT leader’s guide to deep learning

From the ebook:

"Deep learning is a subset of machine learning. It uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be expensive and requires massive datasets to train itself on. That’s because there are a huge number of parameters that need to be understood by a learning algorithm, which can initially produce a lot of false-positives. For instance, a deep learning algorithm could be instructed to “learn” what a cat looks like. It would take a massive data set of images for it to understand the minor details that distinguish a cat from, say, a cheetah or a panther or a fox.

In March 2016, a major AI victory was achieved when DeepMind’s AlphaGo program beat world champion Lee Sedol in four out of five games of Go using deep learning. The deep learning system worked by combining “Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play,” according to Google.

Deep learning also has business applications. It can take a huge amount of data—millions of images, for instance—and recognize certain characteristics. Text-based searches, fraud detection, spam detection, handwriting recognition, image search, speech recognition, Street View detection, and translation are all tasks that can be performed through deep learning. At Google, deep learning networks have replaced many “handcrafted rule-based systems.” "

http://www.techproresearch.com/downloads/it-leader-s-guide-to-deep-learning/