shakespeare | powered SMS bot with sentiment analysis | Runtime Evironment library

I have an express.js backend that handles routes and some mock data that is accessed via certain routes. Additionally, there is a get request and post request for receiving and adding documents respectively to the Firestore collection, "books".

I'm trying to filter out a list of stop words from a longer list of words, where the newly-filtered words and their counts become the key-values of a dictionary. The code I have will do this, but there are two issues:

1. I thought I heard that nested for loops are frowned upon and to be avoided if possible
2. The loop seems to take a while to finish (16.89223 seconds - on a 2019 MacBook Pro) . There are, however 3,476 key-value pairs as a result.

Am I over thinking this thing, or are there quicker ways to get the job done?

Here is the code:

I am trying to write data from csv file to MySQL database with python. I created a table in MySQL with the query:

i'm building a blog with gatsbyjs where blog posts are .md files and are statically rendered as HTML pages. i've managed to style the title, date, and published data, but anything under the --- is in times new roman. i've looked everywhere for inline styling tags for MDXRenderer but have had no luck. is this supported and if not, how can i style my body content? thanks!

index.md

I initially tried making an RNN that can predict Shakespeare text, and I did it successfully using character level-encoding. But when I switched to word level encoding, I ran into a multitude of issues. Specifically, I am having a hard time getting the total number of characters (I was told it was just dataset_size = tokenizer.document_count but this just returns 1 ) so that I can set steps_per_epoch = dataset_size // batch_size when fitting my model (Now, both char and word level encoding return 1). I tried setting dataset_size = sum(tokenizer.word_counts.values()) but when I fit the model, I get this error right before the first epoch ends:

WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 32 batches). You may need to use the repeat() function when building your dataset.

So I assume that my code believes that I have slightly more training sets available than I actually do. Or it may be the fact that I am programming on the new M1 chip which doesn't have a production version of TF? So really, I'm just not sure how to get the exact number of words in this text.

Here's the code:

Please help understand the cause of the error when applying the adapted TextVectorization to a text Dataset.

Introduction to Keras for Engineers has a part to apply an adapted TextVectorization layer to a text dataset.

I am using the R programming language. I learned how to take pdf files from the internet and load them into R. For example, below I load 3 different books by Shakespeare into R:

I am using the R programming language. I am trying to learn how to summarize text articles by using the following website: https://www.hvitfeldt.me/blog/tidy-text-summarization-using-textrank/

As per the instructions, I copied the code from the website (I used some random PDF I found online):

I am new to R and Webscraping. As practice I am trying to scrape information from a fake book website. I have managed to scrape the book titles, but I now want find the mean word length for each individual word in the book titles. For example, if there were two books 'book example' 'random books' the mean word length would be 22/4 = 5.5. I am currently able to find out the mean length of the full book titles, but I need to split them all into individual words, and then find the mean length.

Code: