5 Amazing Deep Learning Frameworks Every Data Scientist Must Know! (with Illustrated Infographic)

  TensorFlow We’ll start with TensorFlow.

TensorFlow works well on images as well as sequence-based data.

If you are a beginner in deep learning, or don’t have a solid understanding of mathematical concepts like linear algebra and calculus, then the steep learning curve of TensorFlow might be daunting.

I totally understand that this aspect can be complex for folks who are just starting out.

My suggestion would be to keep practicing, keep exploring the community, and keep reading articles to get the hang of TensorFlow.

 Once you have a good understanding of the framework, implementing deep learning models will be very easy for you.

  Keras Keras is a pretty solid framework to start your deep learning journey.

If you are familiar with Python and are not doing some high-level research or developing some special kind of neural network, Keras is for you.

The focus is more on achieving results rather than getting bogged down by the model intricacies.

So if you are given a project related to, say image classification or sequence models, start with Keras.

You will be able to get a working model very quickly.

Keras is also integrated in TensorFlow and hence you can also build your model using tf.


  PyTorch As compared to TensorFlow, PyTorch is more intuitive.

One quick project with both these frameworks will make that abundantly clear.

Even if you don’t have a solid mathematics or a pure machine learning background, you will be able to understand PyTorch models.

You can define or manipulate the graph as the model proceeds which makes PyTorch more intuitive.

PyTorch does not have any visualization tool like TensorBoard but you can always use a library like matplotlib.

I wouldn’t say PyTorch is better than TensorFlow, but both these deep learning frameworks are incredibly useful.

  Caffe Caffe works very well when we’re building deep learning models on image data.

But when it comes to recurrent neural networks and language models, Caffe lags behind the other frameworks we have discussed.

The key advantage of Caffe is that even if you do not have strong machine learning or calculus knowledge, you can build deep learning models.

Caffe is primarly used for building and deploying deep learning models for mobile phones and other computationally constrained platforms.

  Deeplearning4j Like I mentioned before, Deeplearning4j is a paradise for Java programmers.

It offers massive support for different neural networks like CNNs, RNNs and LSTMs.

It can process a huge amount of data without sacrificing speed.

Sounds like too good an opportunity to pass up!.  End Notes & Illustrated Infographic Are there any other deep learning frameworks you’ve worked on?.I would love to hear your thoughts and feedback on that plus the ones we covered in this article.

Connect with me in the comments section below.

And remember, these frameworks are essentially just tools that help us get to the end goal.

Choosing them wisely can reduce a lot of effort and time.

As promised, here is the infographic with detailed about each deep learning framework we have covered.

Download it, print it, and use it next time you’re building a deep learning model!.You can also read this article on Analytics Vidhyas Android APP Share this:Click to share on LinkedIn (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on Pocket (Opens in new window)Click to share on Reddit (Opens in new window) Related Articles (adsbygoogle = window.

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