The computations required for Deep Learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018. AI could account for as much as one-tenth of the world’s electricity use by 2025 according to this article .
Thanks to the Editors and Board of WIREs for supporting me. As an independent reviewer, I will be fair to everyone and never give in to the “scientific mafia” and “citation cartels”.
Data Mining and Knowledge Discovery (WIREs) (Impact Factor: 2.541)
- Download the apk file: TF_Classify.apk.
I am using the song above to thank you all for your help and support in the past. You know that I have spent the last three years (2014-2017) in pursuing my Ph.D. degree in Computer Science and got a plan to be graduated in 2018.
A few days ago, I updated my Windows 10 to version 1709 and found out that Microsoft added the GPU monitor in the Task Manager which I thought is awesome for ML developers and researchers.
Here is a screen capture of the official MNIST codes running Tensorflow-GPU on my Desktop. It is clear to see that the GTX 960 uses about 3.5GB memory out of 4.0GB to train the ConvNets, which is much faster than the CPU computing.
As we all know, the TensorFlow is very powerful and mature deep learning library with strong visualization capabilities and several options to use for high-level model development. PyTorch is still young framework which is getting momentum fast.
I strongly suggest CS and IT researchers/engineers learn both of them.
Tensorflow will be a good option if you are developing models for production or on mobile platforms, maybe in the future for large-scale distributed model training. Because it has good community support and comprehensive documentation, it is easier to find answers and get helps online.
Well, PyTorch is a good fit if you are doing research or your production are not very demanding.
Personly, I think Pytorch has better development and debugging experience.
The amazing website http://playground.tensorflow.org can help you open a Neural Network on your Web Browser. The GUI is mind blowing, and you could download all the codes to study or to build your own project.
Now, The Good News! Amro and Ray Phan have created the MATLAB version of the NN playground, it looks just like the GUI of the Tensorflow version. However, it is not tensorflow-based, it is built on the Neural Networks Toolbox of Matlab (>R2009b). The authors said they are inspired by the [TensorFlow Neural Networks Playground] interface readily available online, so they created a MATLAB implementation of the same Neural Network interface for using Artificial Neural Networks for regression and classification of highly nonlinear data. Continue reading “TensorFlow Neural Network Playground in Matlab”