clickbait | Classify strings as being click bait

I'm facing this issue in my college project which is a clickbait news classifier, i.e., it classifies between clickbait headlines and non-clickbait headlines. I'm using a dataset which has 16000 headlines of each type. Now, the main issue I'm facing is on training my network on 70% data, with 30% as my test set size. My validation set is 30% of the training set. But after fitting and evaluating the model on my test set, and I get this

After fitting:

Epoch 10/10 131/131 [==============================] - 1s 6ms/step - loss: 0.2098 - accuracy: 0.9457 - val_loss: 0.3263 - val_accuracy: 0.9417

After evaluating on test set:

300/300 [==============================] - 1s 2ms/step - loss: 0.3030 - accuracy: 0.9432

Confusion Matrix:

array([[4638, 162], [ 383, 4417]])

Now I'm very new to neural networks and i'm not sure if these accuracies are supposed to be this similar to each other. Is this something that I should be concerned about or am I missing something? I appreciate all the help I can get... Thanks!

I am trying to make a game in HTML with Java at the same time, and I do not know exactly how to color/paint the element. Here is what I have so far with my game:

I was trying to work on a rainbow-pen drawing canvas, but whenever I draw, the lines appear dotted. Only when I move really slowly, does it appear properly.

The 'mousemove' event listener, isn't really being able to detect fast changes or is there some other issue in my code? Also, here is the codepen link, if anyone wants a working program.

Here is the codepen!

For practice, I'm taking working JavaScript code and converting what I can to jQuery. I've gone through StackOverflow and the jQuery documentation and am not finding what I need. Here's my original/working JS snippet:

Consider the following example:

while i am compiling this code i am getting error

I'm planning to use NoSQL as primary database for all my application's data primarly because of terms like "scalability", "cloud", "big data" in mind. I don't care about "schema-less" as I use ORM frameworks anyway with traditional RDBMS.

Almost every application use relations, whether it is social network, e-commerce or something else there are always relations.

So I looked up different types of NoSQL databases that could qualify.

• Key-Value: Definitely not for this purpose.

• Column-based: I rejected it immediately because it just looks unnatural to me and I will never use it

• Graph-based like Neo4j: They claim it's good for relations but it's just a clickbait and misses a lot of features that make me want to just use RDBMS after all. And some people only use it for geo search, and still use RDBMS for the main data. Apparently read performance is bad so I reject it.

• Document-based like MongoDB, Couchbase: Seems like the most popular alternative for RDBMS (to replace it completely). Also MongoDB will be ACID compliant soon (https://www.mongodb.com/transactions).

Now the question is can something like MongoDB, Couchbase be used in scenarios with many relations(almost every application) as a complete replacement for RDBMS?

After like few days of reading about NoSQL and various use cases in big sites I came into conclusion that these databases are almost never used as primary/main databases and most often are used in connection with RDBMS for doing just some specific things.

Is it even valid to compare NoSQL and SQL databases considering ambiguity of this term?

SQL databases are not ambiguous while NoSQL databases are!

Good article that I found!: https://www.marklogic.com/blog/nosql-without-multi-document-multi-statement-transactions/

The promise of NoSQL is speed; but like a car without brakes, not very safe unless it allows multi-document transactions.

--

if you want to be taken seriously as a database, you need to support multiple statement transactions.

I was looking at this codegolf problem, and decided to try taking the python solution and use urllib instead. I modified some sample code for manipulating json with urllib:

I would like to train a model to generate text, similar to this blog post

This model uses - as far as I understand it - the following architecture
[Sequence of Word Indices] -> [Embedding] -> [LSTM] -> [1 Hot Encoded "next word"]

Basically, the author models the process as classification problem, where the output layer has as many dimensions as there are words in the corpus.

I would like to model the process as regression problem, by re-using the learned Embeddings and then minimising the distance between predicted and real embedding.

Basically:

[Sequence of Word Indices] -> [Embedding] -> [LSTM] -> [Embedding-Vector of the "next word"]

My problem is, as the model is learning the embeddings on the fly, how could I feed the output in the same way I feed the input (as word indices) and then just tell the model "But before you use the output, replace it by its embedding vector" ?

Thank you very much for all help :-)