Part of the answer is here - https://www.wheresyoured.at/the-men-who-killed-google/
119 karma · joined December 4, 2008
Part of the answer is here - https://www.wheresyoured.at/the-men-who-killed-google/
It's easier for me to recognize the ideas, concepts, keywords it has written than to recall them from memory when my "memory cache" is empty.
I can't believe someone says this. It's so not true in my experience. These feelings have a lot in common, but they are definitely not the same.
Sometimes this awareness is enough to change bad mental habits. But there are other things that you continue to do even if you understand that you don't want it. These are "imprints" NLP terminology.
To change these imprints my friend does really long Vipassana sessions for more than 10 hours. She claims to change at least one strong imprint that was "panic when staying at home alone". Which is related to an event happened when she was less than a year old.
I have not tried so long Vipassana sessions and to change my imprints I do Connirae Andreas "Core Transformation" technique. It's easier to have other person do it to you, however with enough determination you can train to do it by yourselves. Unfortunately it's still a lot of efforts to learn it. Anyway may be that helps.
Just a few things: in general case it's better not to use MSE after sigmoid due to slow convergence.
And "logits" variable is not logits actually, it's probabilities. Logits is what you have before applying sigmoid activation.
They process Lucene index and create embedded representation of it. Then you can search over that representation for "semantic" matches.
Last time I checked it about a year ago the embedded collection of documents was kept in the memory and the search was implemented by a linear scan. So I suspect it can be slow on very large collection of documents.
The current agriculture is quite bad for soils and puts many chemicals into our food.
P.S. Absolute fasting can be dangerous.
"Adversarial examples generalize across models trained to perform the same task, even if those models have different architectures and were trained on a different training set."
It's easy to be under illusion that you can create everything using a verbose language. At some point the complex system you work on just gets extremely hard to reason about.
For verbose languages you reach this point considerably faster.
In the simple case your hyperparameter can be α in
sentiment = (α * #positive_matches - #negative_matches) / (document_word_count)
So there is an option to try making it normal by taking logarithm for example and calculating mean, etc. after that.
P.S. Thanks for the data, you saved a good amount of our time.
IIRC it was related to increased oxygen level.
[1] "История Земли и жизни на ней" ("History of the Earth and its lifeforms")
"Experts" and "expertise" in "Find Experts. Sell Expertise" is too general. I would try to come up with more concrete examples though I realize it's not an easy task.
A google search suggests that he was born in 1961.
I ran a few queries using the code and its default dataset, trying to use neutral words for substraction: "mosquito -small +mountaineer", "mosquito -big +mountaineer", "mosquito -loud +mountaineer", "mosquito -normal +mountaineer", "mosquito -usual +mountaineer", "mosquito -air +mountaineer", "mosquito -nothing +mountaineer".
The most frequent words for these queries are:
6 times: "borisovich" "chaudhry" "entomologist" "hymnist" "sitwell"
5 times: "mineralogist" "ornithologist"