I know a lot of people use J to represent cost function. what does the capital letter ‘J’ exactly mean?

It’s something I’ve heard here and there but never really got an explanation.

From online, I found this and this

https://hackernoon.com/various-optimisation-techniques-and-their-impact-on-generation-of-word-embeddings-3480bd7ed54f

https://stats.stackexchange.com/questions/288658/better-performance-with-gradient-descent-than-adam-on-word2vec

Optimizers that build upon Adagrad aim to fix the vanishing learning rate problem, so why would they do worse?

Perhaps minimas are really unstable, and would benefit from the smaller learning rates. Could this issue then be alleviated by increasing the window of past gradients?

I think I saw it on twitter a few months ago. The list had links to papers and it was pretty long. Anyone got a link?

Like the title asks, if I have a string like “address 1234 home street”, can I get spacy to recognize that the key is address and the value is 1234 home street? Or I guess the better question is, if I just have a string like 1234 home street, can i get spacy to recognize that as an address without further context around the string? It’s not in a sentence since it’s derived from form data extracted via OCR. More difficult, get spacy to recognize a string that isn’t a traditional category like an address, but a custom category?

How do I start researching this?

here is my question in quora

https://www.quora.com/Why-is-RNN-less-progress-research-than-CNN-especially-the-time-series

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question is

Why is RNN less progress research than CNN (especially the time series)?

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and answer is

Any problem concerning the images is incredibly harder than that concerns a time series. This is why the research on CNN and its derivatives (U-Net, GAN) are still continuing.

The research on how to deal with time-series data is almost finished. It looks like researchers are trying to come up with better and better techniques, but what is actually happening is people are trying to predict dependencies that are actually not present in the data or using insufficient data!

A good example is the stock value prediction. The simple truth is that the stock prices depend on many more variables that are not present in the typical input time series’ used with RNN or LSTM.

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My personal opinion is now LSTM is SOTA but I think another SOTA network will be created.

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how about think of this topic?

Hi everyone,

I’m those host of the All Things Interesting Podcast and wanted to drop in to share my first interview with blockchain startup technical cofounder Kasian Franks.

Kasian and his team at Vectorspace AI are working on bridging blockchain with context controlled vector based NLP/NLU to power AI and machine learning. In this episode, we talk about all things AI, ML, NLP/NLU, and blockchain.

All Things Interesting Podcast #1: Kasian Franks of Vectorspace AI

Good day!

Here’s an article I wrote on setting up Amazon SageMaker with Terraform, based on the example from AWS Solutions.

https://medium.com/@qtangs/machine-learning-infrastructure-with-amazon-sagemaker-and-terraform-a-case-of-fraud-detection-ab6896144781

Source code can be found here: https://github.com/qtangs/tf-fraud-detection-using-machine-learning

I’d love to hear feedback from the community.

Please note, though, that I’m a noob in machine learning, so pardon my ignorance in many areas.

Original AWS example: https://aws.amazon.com/solutions/fraud-detection-using-machine-learning/

Thanks!