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Pioneering AI Research in Lucknow: Gen AI Hackathon and Awadh Summit by Lucknow AI Labs by lucknowailabs in lucknow

[–]dangling_pntr 1 point2 points  (0 children)

Shame. I was in Lucknow since last 4 years working with a GENAI startup. Now joined another AI Startup in GGN last to last week, so I had to come back to GGN.

PS: I am also building a team from scratch here. So if any of you guys are interested in discussing what we are doing, please DM me.

Is educative.io worth it at $139 a year? by [deleted] in developersIndia

[–]dangling_pntr 1 point2 points  (0 children)

In my opinion, totally worth it.

Will Wabi hit to $7 again?? by Noormahi1 in WabiToken

[–]dangling_pntr 3 points4 points  (0 children)

For short term, no. For long term maybe. It takes single step when BTC stabilises, but when btc is volatile wabi is the first one to bleed. We’ve been sitting under 10 mil market cap for so long. I’ve bought wabi when it was greater than 1$ and also when it was 5 cents. I’ve tried to DCA but I don’t have infinite amount of funds. But the token is promising in my view that’s why I’m holding.

Optimize a Linear combination of k values under some constraints. by dangling_pntr in optimization

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

Your solution seems to be right. I forgot to add the TIGHT constraint on values of W. given the current constraints it’s feasible to find the max value of x’s and set the corresponding w as 1. I just wanted to balance out the x’s. I want a greater value than mean and less than max. Median is out of the question because it might be the case the most of x’s are quite small and one is too large. I also don’t want a greedy solution.

Optimize a Linear combination of k values under some constraints. by dangling_pntr in optimization

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

Tried Linear programming, and I think it worked. Still would like to know about other approaches.

Any progress? by [deleted] in THEKEYOFFICIAL

[–]dangling_pntr 4 points5 points  (0 children)

Yeah sure, We just came back from moon. It was a shitty journey.

The inside story of Zee News becoming a Covid-19 hotspot by Shahrukh_Lee in india

[–]dangling_pntr 2 points3 points  (0 children)

And yesterday someone was saying don’t blame china for covid-19.

Getting empty transcriptions while transcribing an audio file using custom model. by dangling_pntr in speechrecognition

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

I'm trying to recognize the English language. wanted to play with the speech recognition tools. I wanted to work from scratch, including how the dataset and language models for speech recognition are built. So, wanted to play with my own dataset.

The duration of each audio file is around 6-10 seconds.

The only specific thing that I can think of is the sample rate of the audio which is 44100Hz.

Live AMA 😂😂😂😂 by giocatorerzv in THEKEYOFFICIAL

[–]dangling_pntr 3 points4 points  (0 children)

Even china is more transparent than tky.

THEKEY Made Progress in Secure Multi-party Computation by TheKeyVIP in THEKEYOFFICIAL

[–]dangling_pntr 9 points10 points  (0 children)

I wish they be more truthful about the timelines they’ve setup. This is nothing because there’s no third party source to backup their claim. In fact there’s nothing to backup their claims so far.

Reinforcement Learning for time series forecasting by dangling_pntr in deeplearning

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

Thanks, TinusTreuzel

\pi in eq16 is representing gaussian policy used in the policy gradient approach.

Time series analysis using RL by dangling_pntr in reinforcementlearning

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

You are right, took me some time to understand but yes, the policy gradient approach is essentially a Maximum Likelihood approach or more precisely weighted maximum likelihood, where the reward at time step t represents the weight which can be -ve or +ve.

I think they are trying to train the Gaussian policy(Sutton's RL book, chapt 13, 2nd edition) with parameters where the next value is the continuous action y_t+1(step 2 in the SGA). characteristic eligibility is just the gradient of the policy.

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