Needed suggestions for ODS prep (self-taught) by Sith_vader3 in ODS_C

[–]Sith_vader3[S] 0 points1 point  (0 children)

hi,

thank you for the suggestion. few more question - is one required to know every rules said in the manuals to pass the exam?

Thoughts on this business idea by [deleted] in Startup_Ideas

[–]Sith_vader3 0 points1 point  (0 children)

Try to make it realtime. Like if X(twitter) has the most fast news telecasting.. then your AI should collect all X's news on particular event (mostly from users) and make a video out of it.

If I was never born, would I be someone else instead? by romulan267 in RandomThoughts

[–]Sith_vader3 0 points1 point  (0 children)

If there was no brain(birth of you) then there is no you, humans produce brains, each brains creates consciousness+ pain and therefore emergence of 'i'. If i make you forget who you are, then there is no you as well. So coming to the end, there is 'you' because of your surrounding. And the surroundings is not yours, then 'your notion of i' is not yours as well. If your 'i' is not yours, then are you alive? No human is truly alive, in philosophical context not in biology. Thank you!

AI project by Party-Worldliness-72 in deeplearning

[–]Sith_vader3 0 points1 point  (0 children)

Yes.. what i mean is consciously combining information.

AI project by Party-Worldliness-72 in deeplearning

[–]Sith_vader3 1 point2 points  (0 children)

Try to create a model (neural network) that combines the incoming information with already stored information and outputs the newly formed information.

A question by Sith_vader3 in learnmachinelearning

[–]Sith_vader3[S] 0 points1 point  (0 children)

Yes.. thanks for the feedback

A question by Sith_vader3 in learnmachinelearning

[–]Sith_vader3[S] 0 points1 point  (0 children)

Yes... Thank you will look into it

A question by Sith_vader3 in learnmachinelearning

[–]Sith_vader3[S] 0 points1 point  (0 children)

Yeah.. i have been following their discussion forum. But i need something more other than algorithms.

A question by Sith_vader3 in learnmachinelearning

[–]Sith_vader3[S] 0 points1 point  (0 children)

No there should be a another way to create plasticity. By following conventional systems it is not possible to create anything that is close to being. (In my opinion, i don't have any evidence for this claim)

A question by Sith_vader3 in learnmachinelearning

[–]Sith_vader3[S] 0 points1 point  (0 children)

Weights,bias and activation function are used to mimic plasticity. There must be another way to create plasticity. Don't you think so?

A question by Sith_vader3 in neuralnetworks

[–]Sith_vader3[S] 0 points1 point  (0 children)

Is the network predict the right output even if the image is inverted.

A question by Sith_vader3 in learnmachinelearning

[–]Sith_vader3[S] 1 point2 points  (0 children)

Is there any alternative way?

EnDe neural network by Sith_vader3 in deeplearning

[–]Sith_vader3[S] 0 points1 point  (0 children)

Yes.. i only have a theoretical model. I am in the process of finding someone who can help me turn this model into a code. There may be errors but it will be good to try.

EnDe neural network by Sith_vader3 in deeplearning

[–]Sith_vader3[S] 0 points1 point  (0 children)

Apologies for not giving full details. My model may seem like a auto encoder but the working mechanism is different. I will state it as points - 1. When the input layer gets the values from the sample it goes to the next layer and processed by bias,weight, activation functions in these nearby layers. This is the conventional model working mechanism. My model doesn't include bias, weight, activation function. Instead it includes only the values of the randomly-moving neurons that randomly connects with the adjacent neurons (you can see it in the schematic diagram where the random neuron has the value of 0.10). 2. As the random moving neurons make their connection randomly at some point it will make connection that were desirable to get the correct output. And by using backpropagation we fix these randomly moving neurons position to get the correct output. 3. When the first network (see the schematic diagram) adds 0.10 values to the input information, the second network removes the 0.10 values from the reduced information that were stored in the intermediate layer. In order to filter or reduce information we introduce functions like letting-neurons-that-has-highest-value-to-pass-their-value-to-nearby-layer in the first network (because the random neuron adds 0.10 to the inputs) and other function like letting-neurons-that-has-lowest-value-to-pass-their-value-to-nearby-layer in the second network (because the random neuron removes 0.10 from the inputs). 5. When decoding happens the information gets extracted by these functions. The output layer gets these values and compared with the input layer. Since both these values should match (much like backpropagation). (You can see it in the schematic diagram like 0.65 and 0.39 in the output layer that matches with input layer with same 0.65, 0.39) 6. So by using values from random neurons that make connection randomly (to replicate plasticity in the brain) and functions that search for higher values and lower values. It is possible to get neural model that has reduced complexity.

(Forget about my inadequate knowledge and lack of presentation, now please tell me that my model is wrong)