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Stellar
getstellar.ai › blog › generative-ai-python-examples-unlocking-creativity-with-code
Generative AI Python Examples – Unlocking Creativity with Code - Stellar
August 13, 2024 - The tensorflow and keras libraries provide excellent tools for implementing GANs. Here’s a basic example of setting up a GAN for image generation:
Discussions

Made a search engine with Python that uses generative AI to answer your questions instantly. It's free, anonymous, and live at beta.sayhello.so
I love it and it's faster then Google and with out ads More on reddit.com
🌐 r/Python
62
377
May 4, 2022
An example of a generative AI tool: Suggesting better question titles - Meta Stack Exchange
With all the conversation around generative AI and our early explorations into it, I thought it might be useful to post some screenshots that show examples of the type of early experiments that we’re More on meta.stackexchange.com
🌐 meta.stackexchange.com
April 19, 2023
An Introduction to Python in the Age of Generative AI
Looks pretty well organaised. I'm not sure about the connection between the "Age of Generative AI" and the site's content. How is this related to what seems to be regular python learning topics? More on reddit.com
🌐 r/learnpython
21
28
June 30, 2024
I learnt all the basics about python and had interest in learning about "AI development with python". and now I'm stuck cuz I don't know where to start. Can anyone give some advice to me ?
Do you already have a background in ai? If not I recommend at least learning the basic language agnostic concepts. More on reddit.com
🌐 r/Python
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December 30, 2022
People also ask

What is the structure and format of this Generative Artificial Intelligence program?
The Applied Generative AI program is offered in a flexible online format that includes · Recorded video lectures  · Interactive mentoring sessions, and  · 2 live masterclasses by JHU faculty
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online.lifelonglearning.jhu.edu
online.lifelonglearning.jhu.edu › johns hopkins university with great learning
Johns Hopkins Applied Generative AI Course & Certificate Program
What are the highlights of the Generative AI course?
The Certificate Program in Applied Generative AI from Johns Hopkins University is a comprehensive online learning experience designed to equip professionals with advanced skills in Generative AI. Here are some of the highlights of the program:  · Flexible Online Format: The program is delivered online through recorded video lectures, live mentorship sessions, and live monthly faculty-led masterclasses.  · World-Class Faculty: The program is taught by world-class faculty and industry experts who have real-world experience leading AI practices at Fortune 500 companies.  · Research-Driven Curricu
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online.lifelonglearning.jhu.edu
online.lifelonglearning.jhu.edu › johns hopkins university with great learning
Johns Hopkins Applied Generative AI Course & Certificate Program
What topics are covered in the curriculum of this Generative AI course by JHU?
The topics in the curriculum of this Applied Generative AI program include:  · Generative AI Landscape  · Python Programming with Generative AI  · Foundation of AI  · Natural Language Processing And Image Classification  · Transformers for Large Language Models  · Prompt Engineering  · Classification, Content Generation, and Summarization with Gen AI  · Secure and Responsible Gen AI Solutions  · Developing Agents with LangChain  · Retrieval Augmented Generation (RAG) Search  · Advanced RAG  · Fine-Tuning and Customization of Generative AI
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Johns Hopkins Applied Generative AI Course & Certificate Program
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Medium
medium.com › @subramanian.m1 › introduction-to-generative-ai-with-python-9b9fd0950414
Introduction to Generative AI with Python | by Subramanian M | Medium
April 3, 2024 - Known for its simplicity, flexibility, and dynamic computation graph, PyTorch is favored for research and prototypes in generative AI. ... Essential for handling numerical tasks in Python, NumPy is often used for its efficient array manipulation capabilities, serving as the backbone for more complex operations in AI models.
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W3Schools
w3schools.com › gen_ai
Generative AI Tutorial
You will learn to understand Generative AI capabilities and write prompts that minimize misinformation and biased results. You will learn how to preface your prompts and add details to them to generate consistent results. In these tutorials, we will use examples to better explain the various concepts.
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GitHub
github.com › NVIDIA › GenerativeAIExamples
GitHub - NVIDIA/GenerativeAIExamples: Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
cd GenerativeAIExamples/RAG/examples/basic_rag/langchain/ docker compose up -d --build · Go to https://localhost:8090/ and submit queries to the sample RAG Playground. Stop containers when done. ... A Data Flywheel is a self-reinforcing cycle where user interactions generate data that improves AI models or products, leading to better outcomes that attract more users and further enhance data quality.
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online.lifelonglearning.jhu.edu › johns hopkins university with great learning
Johns Hopkins Applied Generative AI Course & Certificate Program
3 days ago - Research-Driven Curriculum: The curriculum is designed based on the latest research and developments in Generative AI, ensuring that learners benefit from the most up-to-date and relevant information. In-Demand Tools and Libraries: Develop proficiency with in-demand tools and frameworks such as Python, Google Colab, BERT, VS Code, vector databases (Chroma, Pinecone), Transformers, Retrieval-Augmented Generation (RAG), and quick fine-tuning methods.
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InfoWorld
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6 generative AI Python projects to run now | InfoWorld
October 26, 2023 - Get a hands-on introduction to generative AI with these Python-based coding projects using OpenAI, LangChain, Matplotlib, SQLAlchemy, Gradio, Streamlit, and more.
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GeeksforGeeks
geeksforgeeks.org › pandas › pandas-ai
Pandas AI: The Generative AI Python Library - GeeksforGeeks
July 23, 2025 - Pandas AI wants to make it possible for you to visually communicate with a machine that will then deliver the desired results rather than having to program the work yourself. To do this, it uses the OpenAI GPT API to generate the code using Pandas library in Python and run this code in the background.
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GitHub
github.com › opea-project › GenAIExamples
GitHub - opea-project/GenAIExamples: Generative AI Examples is a collection of GenAI examples such as ChatQnA, Copilot, which illustrate the pipeline capabilities of the Open Platform for Enterprise AI (OPEA) project.
CodeGen: Gen-AI Powered Code Generator. ... DocIndexRetriever: Document Retrieval with Retrieval Augmented Generation (RAG). ... InstructionTuning: Application of Instruction Tuning.
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Reddit
reddit.com › r/python › made a search engine with python that uses generative ai to answer your questions instantly. it's free, anonymous, and live at beta.sayhello.so
r/Python on Reddit: Made a search engine with Python that uses generative AI to answer your questions instantly. It's free, anonymous, and live at beta.sayhello.so
May 4, 2022 -

https://beta.sayhello.so

Hey, we're Michael and Justin, a two-person team working on this project. Hello is a search engine that extracts understanding + code examples from technical sources, bringing you actionable insights for the problem you’re working on.

When you ask a question, we pull and rerank raw site data from Bing, then extract understanding with our large language models. For extracting and ranking code snippets, we use BERT-based models. Finally, we use seq-to-seq transformer models to simplify all this input into a final explanation.

Hello's backend is built in Python, using PyTorch to run our generative seq-to-seq transformer models and FastAPI/Uvicorn/Gunicorn for the routing.

We started Hello Cognition to scratch our own itch, but now we hope to improve the state of information retrieval for the greater developer community. If you'd like to be part of our product feedback and iteration process, we'd love to have you—simply join our Discord or contact us at [email protected]. This is a great way to get early access to new features too :)

We're looking forward to hearing your ideas, feedback, comments, and what would be helpful for you when navigating technical problems!

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Google
docs.cloud.google.com › generative ai › generative ai code samples and sample applications
Generative AI code samples and sample applications | Google Cloud Documentation
Learn how to convert text and images to vector embeddings using the Vertex AI SDK, for use in a retrieval-augmented generation (RAG) application. ... Learn how to augment Gemini's response with real-time data, such as a company's stock price and latest news. ... Learn how to migrate your existing Vertex AI SDK code to call Gemini instead of PaLM. ... Learn how to tune Gemini using Vertex AI, to train the model to respond well to questions about Python coding.
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Medium
mahaboob.medium.com › developing-generative-ai-using-python-a-step-by-step-guide-e7bd5b973d7f
Developing Generative AI Using Python: A Step-by-Step Guide | by Mahaboob Basha | Medium
January 16, 2024 - Generative AI aims to generate new content such as images, music, or text by learning patterns and structures from existing datasets. The two main types of generative models widely used today are Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). ... To begin, ensure that Python is installed on your system.
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Data Science Dojo
datasciencedojo.com › home › blog › generative ai › top 8 python libraries for generative ai
Top 8 Python Libraries to Easily Build Your AI Models
September 17, 2025 - Explore the top Python libraries for Generative AI, including TensorFlow, PyTorch, and more. Learn how these tools can power your next AI-driven innovation!
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Coda
coda.io › @peter-sigurdson › building-a-simple-ai-generative-language-model-in-python
Building a Simple AI Generative Language Model in Python
November 28, 2023 - Building a generative language model using Python tools such as TensorFlow and PyTorchlinkedin·2⁠⁠ · How to Build Generative AI Model Using Python?
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AWS
aws.amazon.com › what is cloud computing › cloud computing concepts hub › generative ai
What is Generative AI? - Gen AI Explained - AWS
1 week ago - Generative AI training begins with understanding foundational machine learning concepts. Learners also have to explore neural networks and AI architecture. Practical experience with Python libraries such as TensorFlow or PyTorch is essential for implementing and experimenting with different models.
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Coursera
coursera.org › browse › data science › machine learning
Building Generative AI-Powered Applications with Python | Coursera
In this module, you will learn how to build your own ChatGPT-like application using generative AI tools. As part of the project, you will work with Facebook’s BlenderBot model using Hugging Face’s Transformers library in Python. You’ll explore key components such as large language models (LLMs), prompt engineering, and user interface design.
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GitHub
github.com › steven2358 › awesome-generative-ai
GitHub - steven2358/awesome-generative-ai: A curated list of modern Generative Artificial Intelligence projects and services
Unsloth - A Python library for fine-tuning LLMs #opensource. OpenLIT - Open-source GenAI and LLM observability platform native to OpenTelemetry with traces and metrics. #opensource · OpenAI Playground - Explore resources, tutorials, API docs, and dynamic examples. Google AI Studio - A web-based tool to prototype with Gemini and experimental models. GitHub Models - Find and experiment with AI models to develop a generative AI application.
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Top answer
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I'm going to go out on a limb here and suggest that this is a good example of the use of generative AI being integrated into Stack Exchange proper. I'm still going to take the position that you guys should be careful in this exploration and (obviously) be prepared for pushback if some other feature explorations end up going a bit too far for experienced curators' comfort, but I do think this is a solid idea.

The title is easily the most neglected field when new users draft a new question. I still kinda suck at drafting question titles, but I can recognize a garbage one when I see one. That's one of the reasons why, over on the Staging Ground Beta test, I suggested we add a "Please revise your title" comment template, and another user suggested that it links to How do I write a good title?, and it's turned out to be quite the useful template. Sometimes I can salvage a crappy title myself, but I do at times find myself unable to fully grok what a user is looking for, and want to shift the responsibility onto them to write a proper title. If generative AI could help in this area, I personally don't think this is a bad idea.

We probably should have led with this, or included it as a part of my post about Prashanth’s blog post, and for that I take responsibility. We’re learning as we go, and I’ll try not to make that mistake again.

Look... You've gotta understand where a majority of Meta-frequenting-users are coming from: They're relentlessly fighting against an abundance of crappy, AI-generated answers on their favorite sites. Moderators in particular have to sift through more garbage than ever as a result of ChatGPT and other AI tools gaining popularity. They're building advanced tools to try and make detecting and eliminating AI-generated content easier on themselves because they're utterly overwhelmed. There's absolutely going to be some trepidation about some form of generative AI being released on the site they're trying to purge it from existence on. Please take the pushback you got from the CEO's blog post with that in mind.

we are doing explorations and research on how to utilize AI/ML in ways that are promoting a better user experience for things like question asking, search, duplicate detection ...

Oh boy do we need that. Finding duplicates is quite unrewarding, difficult, and leads to squabbles between gold badgers on Stack Overflow all the time. Ensuring that users see potential answers before they even post a question would be great, because as the site grows older, the amount of information that may already answer a new question becomes even more numerous. Lifting the burden of finding a duplicate off curators' shoulders would be a great step forward.

We hope to proceed with the same types of goals for other similar experiments that we may run, and are aiming to keep community members involved as we do so.

That last bit makes me very hopeful. I mean, the fact that you pulled the curtain back a bit here on what y'all are working on so we could get an idea of where you feel generative AI could be useful is great, because we really needed to see that you guys weren't allocating resources to stuff we're going to outright hate. This is a departure from "release and announce" that I'm hoping to see more of, especially on such a hot-button issue as AI is right now.

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I'm... cautiously optimistic. I see the value here, however there's also a potential for this to cause problems when people do a poor job of evaluating whether or not the title they chose matches what they're asking for.

Take example 2; The suggested titles are certainly better than "doesn't work", if we assume the titles actually describe their problem... but I'm not so sure that they do because the user hasn't really presented enough in the question to support anything more than "doesn't work". If you don't know why it doesn't work, you can't assume it's because they added classes to elements, or whatever it is they actually did since that doesn't seem to be part of the screenshot.

A very large percentage of the questions we get on a daily basis are like example 2.

Converting useless titles into useless titles with proper English isn't ideal... but at least it'd be a tool to help improve the questions that aren't this hopeless? I'd certainly appreciate having something like this for editing a question title once the question is improved.


Put another way, I fear that this may put us in a situation where question titles can be inaccurate because we're asking users who do not understand the problem they are asking about to choose a "recommended" title from a list of AI recommendations... potentially adding incorrect context/information to their question. It's one thing for a title to be... lacking information, it's another entirely for wrong information to be added to it.

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Real Python
realpython.com › python-ai-neural-network
Python AI: How to Build a Neural Network & Make Predictions – Real Python
July 24, 2023 - In this step-by-step tutorial, you'll build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You'll learn how to train your neural network and make accurate predictions based on a given dataset.
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Amanxai
amanxai.com › home › all articles › generative ai model from scratch with python
Generative AI Model From Scratch with Python | Aman Kharwal
August 5, 2024 - In this article, I'll take you through the task of building a Generative AI model from scratch with Python. Generative AI Model.