How to embed(build) a project, example beget

Waste video. The GitHub does not contain app.py code

Better is pwinput

I have been looking for a way to make textmode programs in Python, since I messed around with Turbo Pascal in my youth. So I installed the 'rich' library on my Win10 PC. When I type "python -m rich", the colors are less rich (looks like only 13 different colors), the text formats (bold, italic, underline etc) are not showing, just plain basic text. Also the asian characters don't show.

best for loop → for (int i = 0; i < 10; i++)

"n_samples, n_features = X.shape" please what is the use of this line?

Great video thanks - there needs to be an update in the code for file google-cloud-run/test/train.py on line 25 - optim = keras.optimizers.Adam(lr=0.001) --> optim = keras.optimizers.Adam(learning_rate=0.001) This is due to 'lr' being deprecated :)

When I recreated this app, when I predict based on Bachelor’s degree, the salary predicts well. But when I predict based on "Post grad " select, the salary prediction is not accurate. On zero experience, i get a a higher salary than when I have one year experience. This also applicable to the other selections. Only Bachelor’s degree select predicts well. Is this as a result of the outlier?

Excellent.

🎯 Key Takeaways for quick navigation: 🤖 The video demonstrates how to create and deploy a custom chatbot on a website using Python, Flask, and JavaScript. 🛠️ Flask is used as the backend framework, while JavaScript is utilized for the frontend interaction. 📦 Two deployment options are explained: using Flask with Jinja templates internally or building a Flask REST API for separate frontend deployment. 📋 The setup involves cloning a repository, creating a virtual environment, and installing necessary dependencies like Flask, Torch, and NLTK. 🧠 The chatbot's functionality relies on a PyTorch model trained using natural language processing (NLP) techniques. 🎨 The customization of the chatbot is achieved through modifying the intents.json file, which defines different intents, example patterns, and responses. 🔧 Implementation details for the Flask app include defining routes, handling requests, and allowing cross-origin resource sharing (CORS) if deploying frontend and backend separately. 🚀 The tutorial concludes by demonstrating how to run the chatbot either integrated with Flask or as a separate frontend communicating with the Flask backend through POST requests.

I have a ques, Can this project of yours also work today with the new access levels for Twitter/X Api Please respond

Thank you ^^

Thank you so much , you explained it really good.

Do you know why my « loss = » does not decrease and stay close to 0.49 ?

The lecture is awesome, but i have one question, here why the labels are not converted from shape [100] to [100 * 1]?

please explain what features and labels are instead of just saying them.

Does Cloud Run support GPU now?

Your video Literally saved my ass ...Dankeschon

I use wordpress custom html block to add calculator any other thing that I built using html, css and js code. Now can we use pyton code also in wordpress?

thanks

My favorite one-liner is getting the most frequent element from a list :D. my_list = [1, 2, 3, 1, 1, 2, 3, 1, 1] print(sorted({x: my_list .count(x) for x in my_list }.items(), key = lambda x: x[1])[-1][0]) But I think the translate() and maketrans() methods are dope. I saw this on a Clash of code and my jaw dropped, I instantly became a fan of these methods: r = input() print(r.translate(str.maketrans('OiEhS9L6','01345679')))

I have a doubt, I'm not getting the 2:53 part of the video, I don't know howto get that terminal😅

Thank you very much❤❤

This is the best python tutorial i think. Thanks for giving free advance tutorial.

Nice! I didn't think of some of these, good tips.

thank you Patrick

Why not lemmatize vs. stem?

Great teaching ❤

This is really helpful. Thank you!

Amazing

For python 3.11+, pytorch 2.3+ change the dataiter.next() to next(dataiter)

Nice 🎉

Hello Patrick, extending my heartfelt gratitude for this tensor flow series.

can you able to add a pretrained object detection code?

but from where to get the script u used?

great video, thank you for this. I was wondering how I can delay the responses of the chatbot by a few seconds to seem more natural

Do a code completion one

I have watched many tutorials on you tube but this is the best for a course that is hard to learn. You make it so easy to understand. Even a five year old can graspe the concept!

man... idk if i did it right.. but seems like the first improvment dont works with a lot of data... look: @clock def test_1(): for i in range(len(dados)): if i % 2 == 0: dados[i] = 0 @clock def test_1_improved(): for index, i in enumerate(dados): if i % 2 == 0: dados[index] = 0 test_1() test_1_improved() where data is a list of all the number from 1 to 10000. look the output: test_1 executou em 0.00046789998305030167 segundos test_1_improved executou em 0.000614999997196719 segundos test_1_improved did in more time!! what shold i do? thats right?

thanks for this great tutorial! How do I integrate this bot to a already built website?

You totally forgot the ollama python library!!!!

For some reason even with the -v flag it didn't update the container, and it doesn't update even if I delete an recreate the container. Also, when I open VSCode in the running container, there's no folder opened, nor any actual python file. Any idea why?

Very interesting video. Although it lacks some clarifications on the docker commands, this gap could be filled by ChatGPT helping me out understand what they were exactly. Thanks for the video

many thanks!

great video!

Would be great if you could just import torch first 👀

How do I install Playsound? I get an error I can't resolve

Flast 2m , FastAPI 5m. and Django 10m

Thanks Patrick! Was a lovely tutorial and guessing the frame can be used to design other linear regression apps easily (and other models with a little bit of work).

Thank you sir for this tutorial