I've lurked too much - gleaned much from SQLCentral but pretty much keep quite. I was searching for Neural Nets (NN) using a SQL system for main processing and was pleasantly surprised.
Loved the article, sorry to resurrect a thread here, but I've been working on a neural net in sql, but using a simple feed forward where layer 0 is the input and layer n+1 is the output. The whole network could be expressed in 2 tables, one for the neurons, the other for the links between neurons. The Neuron table keeps state from the last feed-forward iteration, a sigmoid shape bias, and a threshold. The NeuralLink table links the neurons and holds a weight per neuron.
I was trying to think in sets when constructing the model. Each neuron will know the layer it is in and by anchoring on the neurons from N1 to Nx, each set of connections are called in and the connected neuron weights are sum'd and saved per neuron.
The problem is - I've so far been unable to unravel the loop to support a recursive CTE because a summation will need to occur before moving onto the next layer. I have to use a while loop to iterate through the layers. (from 1 to x - forward fashion). Essentially, sequential processing must occur at the time being.
The back propagation routine for learning is just done in reverse, Nx to N1
The training data and corresponding expected correct responses can be added into 2 tables and the relational nature immediately allows a pathway for easy training using sets.
This system may work surprisingly well. Usually the layers aren't the issue, its the sheer number of interconnections. If I can take advantage of proper indexing and set based procedures, the SQL solution with large numbers of neurons may end up working because SQL is tuned to working with large amounts of relational data better than my C# code. The Network and the processing code looks a lot less complex, since most of the programming is spent re-building RI into the objects needed to support NN (Neural Nets). I've attempted to make the Neural Net a data processing set system.
To make use of it, update and populate the input layer and call the stored procedure telling it what neural net you want to process. Read output from the last layer in the network for your answers.
Due to the rational layout, several neural networks could be created sharing the sample inputs and outputs. Support to deposit the output data per network with epoch encoding would allow for several networks to process the same input data, potentially allowing for some interesting parallelism.
A Neural Net on SQL would still be quite intensive - albeit predicatively spikey. Its been a fun project. Getting the back-prob to work without a functional calculus background has been tricky.
I work for a soil and water testing lab, and develop databases, web services, intra-business programs (LIMS , analysis, reporting, etc) and other software development. I've made for the company a multiple pH probe system that collects acquired data and determines when the probes are stable and controls other auto-processing aspects.
I have 120 million or so datapoints collected (ph behaviors) and would like to see if I can unleash a neural net on this data and see what kind of stuff would come up.
Some thoughts would be see if there could be a minimal number of samples to predict the end point pH, or determine when something is sampled wrong etc.
Mainly its to expand more useless knowledge 😀
Interesting to see others messing around with NN in SQL.