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    Best Deep Learning Framework For NLP And Why
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    • Best Deep Learning Framework For NLP And Why

    Best Deep Learning Framework For NLP And Why

    Deep learning Frameworks For NLP: Which to Choose?

    Generally, it is hard to say that something is the best without specifying the required conditions. Still, below we will try to list the major pros and cons of the best Deep Learning frameworks have.
    1. Torch7
    Torch7 is a great framework, which is light and has very nice packages. By the way, it is used by  Google and Facebook. Key Pros:
    • It is easy to use, fast and it does not lose accuracy.
    • It boasts great performance
    • It is GPU compatible
    • It is excellent for conv nets.
    • It provides good parallelism for any task.
    Key Cons:
    • It is based on the Lua language, which can sometimes drive you crazy if there is a need to plot or analyze your data. Yes, it requires LuaJIT to run models.
    • It does not work on Windows.
    1. TensorFlow
    Finally, Tensorflow has become mature and now it does boast tons of tutorials/resources. Key Pros:
    • It boasts great performance
    • RNN API and implementation are suboptimal
    • It makes it easy to specify a new network.
    • It runs great with Spark, Google Cloud, and similar platforms
    • It supports two interfaces: Python and C++.
    Key Cons:
    • Sometimes, the computations can be tough because each computational flow here must be created as a static graph.
    • It doesn't work on Windows.
    1. Deeplearning4j
    Boasting a solid Java programming library and a great Python API, Deep learning is great for NLP as it lets us perform language identification, language-specific segmentation, sentence boundary detection, and entity detection. Key Pros:
    • It is faster than TensorFlow on multi-GPUs.
    • Comes with great core text processing tools: SentenceIterator and Tokenizer.
    • Offers smooth microservice architecture adaptation.
    • It has Java and Scala APIs.
    Key Cons:
    • It does not have the text generation and language modeling option.
    1. Theano
    Key Pros:
    • It has an implementation for most state-of-the-art networks.
    • It’s best for making algorithms from scratch as it is simple and easy to use
    • It’s based on Python, offering a very strong and popular scientific computing stack.
    • It has a cross-platform nature, enabling being deployed in a Windows environment.
    Key Cons:
    • Hacky architecture, making it tough to navigate, debug, refactor, and contribute as developers.
    • It’s hard if you need to use standard algorithms.

    The Bottom Line These frameworks are best for NLP, still, all of them have their drawbacks. Torch7 is transparent, but manipulating strings using Lua can be a pain.  Deeplearning4j comes with amazing core text processing tools, but Theano can be deployed in a Windows environment and it is the best if you need to make algorithms from scratch, however, you might find it difficult to use standard algorithms. A list of the TOP deep learning frameworks is reliable. Still, without specifying the requirements, it might be concluded that there is nothing as the best Deep Learning framework. Everything depends on the product you require, its key aspects, size, and, of course, budget. Read our article about image processing object recognition to immerse even more in the world of virtual reality. Has this post given you food for thought? Share your thoughts in the comments

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