I am evaluating Matlab Deep Learning Toolbox vs Tensorflow now. Found some answers on this web and on line, such as
However, unfortunately this answer seems insufficient for my purpose.
There is a fundamental difference in consumer- and in industrial applications, for image sensor in particular, and for almost all sensing and metrology equipment in general. For instance, ghost images (aka flare) in photography may be regarded as a nice visual effect. However, for advanced driver-assistance systems (ADAS) for instance, confusing a ghost image of an incoming car's headlights with a motorcycle's headlight may have vital consequence.
Given this, the accuracy and reliability of math tools, including DL/CNN for image processing, for instance, is far more important in industry then in consumer and entertaiment.
The concept of neural networks is pretty much old. In fact there are maybe 3 core math tools integrated in DL/CNN, and nothing is new: 1) Tensor algebra; 2) Optimization; and 3) Automatic differentiation.
So apart from pros and cons regarding open source vs commercial software, reliable support vs online community, etc., my question boils down to this:
What is the difference between Matlab Deep Learning Toolbox and Tensorflow, in terms of precision, repeatability and reproducibility, accuracy, reliability, stability, etc.?
Ideally, if some examples with supporting data are provided, then it will be great.
The reason for me to ask this question is based on my experience and learning with Matlab. I have used Matlab together with Optimization Toolbox and Image Processing Toolbox extensively. I must admit I love these two toolboxes, very much. Considering Matlab is built on matrix, and tensor is a natural extension of matrix, and considering the power and reliability of Optimization Toolbox, intuitively I have some confidence in Matlab DL Toolbox. Especially it seems to me that the optimization tools in Tensor are quite primitive, mainly based on the gradient-descend (all variations are just different means to adjust the damping factor), or the Levenberg-Marquardt (aka damped least-squares, DLS) without Gaussian-Newton.
I also expect Matlab DL Toolbox may outperform Tensorflow in tensor analysis.
Seems the only drawback of Matlab DL Toolbox is unavailability of automative differentiation - So how important is it in DL/CNN? Does it show advantages in computing derivatives in back-propagation over numerical differentiation? Also, with the existence of Symbolic Toolbox, why is automatic differentiation still unavailable?
Thanks a lot!