Trjtdtk

Airbnb - host wants to reduce rooms, can we get refund?

CRT Oscilloscope - part of the plot is missing

Python password manager

I’ve officially counted to infinity!

Is lying to get "gardening leave" fraud?

My ID is expired, can I fly to the Bahamas with my passport?

What word means "to make something obsolete"?

How would adding a darkvision racial trait to Dragonborn affect balance?

What happens if I start too many background jobs?

Why do computer-science majors learn calculus?

Why is Thanos so tough at the beginning of "Avengers: Endgame"?

Does hiding behind 5-ft-wide cover give full cover?

Packet sniffer for MacOS Mojave and above

How did Arya get back her dagger from Sansa?

Why is this a valid proof for the harmonic series?

Why do freehub and cassette have only one position that matches?

How can I fairly adjudicate the effects of height differences on ranged attacks?

How do i show this equivalence without using integration?

Was Hulk present at this event?

How to creep the reader out with what seems like a normal person?

Is there a database/website with Butcher tableaus?

Unexpected email from Yorkshire Bank

Mass spectrum of a molecule

Is NMDA produced in the body?

Serious doubts on Categorical embedding

Categorical Variable Reduction using NNChoosing an embedding feature dimensionEmbedding layers for categorical featuresKeras: Softmax output into embedding layerSequence EmbeddingWhat does the embedding mean in the FaceNet?Confusion about Entity Embeddings of Categorical Variables - Working Example!Face embedding of unseen imagesRegularization in Embedding models?Make embedding more Gaussian-like

1

1

$begingroup$

I am still having many doubts about the working of categorical embedding.

In particular I have 2 points not clear:

1. Are 1-Hot variables converted to a lower dim vector?

2. What target are neural embedding trained on? Lots of explanations I have seen don't show any target, and I wonder how the back-prop is done.

3. It is not clear at all how the keras functional API works to build an embedding.
Anyone is so helpful to explain step by step how to build one showing matrices dimension?

Thanks in advance.

neural-network keras embeddings neural

share|improve this question

asked Apr 9 at 7:15

3nomis3nomis

31811

$endgroup$

add a comment | 

1

1

$begingroup$

I am still having many doubts about the working of categorical embedding.

In particular I have 2 points not clear:

1. Are 1-Hot variables converted to a lower dim vector?

2. What target are neural embedding trained on? Lots of explanations I have seen don't show any target, and I wonder how the back-prop is done.

3. It is not clear at all how the keras functional API works to build an embedding.
Anyone is so helpful to explain step by step how to build one showing matrices dimension?

Thanks in advance.

neural-network keras embeddings neural

share|improve this question

asked Apr 9 at 7:15

3nomis3nomis

31811

$endgroup$

add a comment | 

$begingroup$

I am still having many doubts about the working of categorical embedding.

In particular I have 2 points not clear:

1. Are 1-Hot variables converted to a lower dim vector?

2. What target are neural embedding trained on? Lots of explanations I have seen don't show any target, and I wonder how the back-prop is done.

3. It is not clear at all how the keras functional API works to build an embedding.
Anyone is so helpful to explain step by step how to build one showing matrices dimension?

Thanks in advance.

neural-network keras embeddings neural

share|improve this question

asked Apr 9 at 7:15

3nomis3nomis

31811

$endgroup$

share|improve this question

asked Apr 9 at 7:15

3nomis3nomis

31811

share|improve this question

asked Apr 9 at 7:15

3nomis3nomis

31811

asked Apr 9 at 7:15

3nomis3nomis

31811

asked Apr 9 at 7:15

3nomis3nomis

31811