Siraj- the rani mukerjee shot was golden!
Keras is the best thing that happened to deep learning !!
Keras for Life !!
Hi Saraj,Can you make a video on how to use sonnet? And also structure and idea if sonnet? Thanks.
A very good overview, thanks. My favorite framework is DL4J.
Would be great to have a retake on this once TF2 is officially released!
Awesome, information packed video. I especially liked the "Conclusions" section.
DAmn where did u find that song😛kuch kuch hota hai is the movie 😀
Hi siraj. Can I ask for a video Series? Here is the idea : train a neural network for predicting crypto market (maybe cntk this time please?), save the model and weights, then…. load the model in a c# winform application and use it to predict.
But Tensorflow 2.0 is coming. And it uses a dynamic graph by default.
I also dont see any commercial frameworks like MATLAB on the list. Just curious if you have looked at how it compares to these major frameworks?
Lol everybody know that sklearn is the best
Shoutout to **dynet**, the even more-natural way to do variable-input modeling – lazy computation graph building (so, more efficient and readable than pytorch). It also has auto-minibatching, which saves a lot of unnecessary wrapping (but pytorch should include soon as well, I hear). Best of all, it works great on CPU, definitely compared to TF and pytorch.My choice for research prototyping.
Keras has MXNet as a backend now too.
07:10 – > „Perl“ 😱
Please do some example video about google automl and how use them in android application.
I use keras and tensorflow 👍
MATLAB is the easier…with all models ready to implement. A graphical tool to create new architectures. In 2 days I did in matlab what took me weeks to do using Tensorflow.
I usually go with Keras and TF..!
if I wanna deploy trained model into Nvidia JetsonTX, which framework is better to use ?
Fantastic list – really useful information. Thanks!!
My research is in optimizing NN for low latency and low power applications. So I have my own NN written in CUDA C++ (both forward and back propagation using different techniques). Which of these frameworks allow to easily integrate, test and compare customized NN written in CUDA C++ with the traditional ones available in their library?
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