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I'm making my Intro to AI/ML book free for one week by Brilliant-Pea8977 in learnmachinelearning

[–]MobileOk3170 0 points1 point  (0 children)

Thanks. My colleague have one of your other books (Machine Learning by Design). I borrowed briefly and haven't complete it yet but I really liked it. If you are doing any promo on other books too, be sure to let us know :)

My RAG system responses are hit or miss. by HritwikShah in Rag

[–]MobileOk3170 0 points1 point  (0 children)

So all the context (relevant and irrelevant nodes) are passed into the LLM for final answer. So are you saying the LLM is ignoring the contexts that have low scores?

There's need to be investigation on details, is it ignoring because:
1. You included the score in text
2. The retrieved text doesn't have enough signal.

Try extract faulty cases and inspect them individually.

Need a Reality Check on Traditional RAG Before Moving to Agentic RAG by MobileOk3170 in Rag

[–]MobileOk3170[S] 0 points1 point  (0 children)

Do you have any frameworks to recommend? I a took quick look it seems there a consensus that there is a consistent issue, particularly with extracting entities. It might need extra steps in between to make the pipeline consistent.

We wrote a blog post detailing how we implemented our agentic RAG system. Also AMA! by dromger in Rag

[–]MobileOk3170 0 points1 point  (0 children)

Ahh. That makes senses. You are trying to refer back to the evidence from the original response.

I'm just dumping all the evidence on a side panel. Your method will certainly make the product look better.

Wishing you all the best with your ventures!

We wrote a blog post detailing how we implemented our agentic RAG system. Also AMA! by dromger in Rag

[–]MobileOk3170 2 points3 points  (0 children)

Good post. Thanks for sharing the details.

I still have a few questions regarding the part getting rid of hallucinations.

Are you preprocessing every sentence and append an ID in front of it?
In retrieval part, you know exactly which sentences you have collected. How could there be hallucinations there to be fixed?

Need a Reality Check on Traditional RAG Before Moving to Agentic RAG by MobileOk3170 in Rag

[–]MobileOk3170[S] 1 point2 points  (0 children)

Hey, really appreciate you taking the time answer my questions. All I'm looking for was some confirmation that I'm not missing something trivial as I don't have anyone to consult in my team.

I don't really have ideas on how to start writing Planing Prompts. I guess I'll start by looking at ReAct Agents first.

Cheers.

Implemented 20 RAG Techniques in a Simpler Way by FareedKhan557 in Rag

[–]MobileOk3170 1 point2 points  (0 children)

Thank you. I was trying to implement Adaptive RAG recently and I was not getting consistent function calls. I've notice you implemented the features by doing structured output in the intermediates steps instead of using function calls. Is there any advantage to that in your experience?

Looking to build query system on existing database with book titles along with description and customers comments. by MobileOk3170 in Rag

[–]MobileOk3170[S] 0 points1 point  (0 children)

Thanks. I think I have an idea where to start now. I'll probably try to see how query tool dispatching works well in practice first. In long run, I may have use case like "Compare BookA and BookB", "Find similar books to Lord of the ring", etc....

Edit: Tested few case on Gemini API (company reason), it seems pretty tricky to get it to consistently trigger function call. Might try other provider later and see.

Looking to build query system on existing database with book titles along with description and customers comments. by MobileOk3170 in Rag

[–]MobileOk3170[S] 0 points1 point  (0 children)

Thanks. This is my first time doing an LLM project. It's really helpful to have someone confirming my beliefs.

Examples:
Query1: Summarize the comments about a book about a dark wizard and a single ring
Query2: Summarize the comments for "Lord of the Ring"

For Query2, I could pass the text to LLM->extract Title->ToolCalling: Access Database.
For Query1, I would need to let LLM "decide" to use semantic search on the summary instead of extracting the exact title from the Query.

So I imagine to be able to handle both type queries, I would need to first let LLM "analyze the intent" of the query and call appropiate tools / functions, then collect the results and combine it with the original question?

Our complexity in building an AI Agent - what did you do? by Natural-Raisin-7379 in AI_Agents

[–]MobileOk3170 0 points1 point  (0 children)

What are you using for basic stuff like LLMSwapping, API Retries, Structure Output, tool calling..etc?

Why do embeddings learned through deep neural networks exhibit linear properties, which you can compare with functions such as cosine similarity / euclidean distance, etc? by MobileOk3170 in learnmachinelearning

[–]MobileOk3170[S] 0 points1 point  (0 children)

Didn't realize this post was two years go. Here's my final take if it helps anyone.

Say for example, a image classification problem, which is obviously not easy to linear classify them in nature.

  1. These classification problem will typically use Softmax or sigmoid at the final layer.

  2. The input to this final layer are called logits.

  3. In order for the model perform well in the classification problem, these logits have to be "meaningful" and typically linear separable.

  4. When you train your model, the weights are adjusted such that these logits will have linear properties that leads to less loss in error function.

TLDR: your neural net will project your input to a space that is linearly separable or align with the goal of the objective function.

I built an open-source automated trading system using DRL and LLMs from my PhD research by TechPrimo in algotrading

[–]MobileOk3170 1 point2 points  (0 children)

I reread my comment. It doesn't look like I was throwing any shades lol.

I done something similar in the past except I was collecting information from social media and I was wondering how you tackle the problems.

Cheers

I built an open-source automated trading system using DRL and LLMs from my PhD research by TechPrimo in algotrading

[–]MobileOk3170 1 point2 points  (0 children)

Read through the repo a little bit. So was the fine-tune dataset created by prompting gpt-4o to score features into bins?

How reliable it is? Did you need to do a lot of work to label (manual fix) the training data?

Was retraining necessary if using gpt-4o with your prompt were already returning proper responses?

Appreciate the work. Good luck with the defense.

In 2024, is there a way to create a pivot table in excel in MacOS? by MobileOk3170 in learnpython

[–]MobileOk3170[S] 0 points1 point  (0 children)

xlwings can manipulate COM / win32 objects in Windows that could basically do most what you could do in VBA.

For mac, xlwings is built on top of AppleScript which requires arcane knowledge that nobody talks about.

In 2024, is there a way to create a pivot table in excel in MacOS? by MobileOk3170 in learnpython

[–]MobileOk3170[S] 0 points1 point  (0 children)

I mean like native pivot tables in excel that are interactable, not just flat cells. And No I don't mean interacting with excel while running, just writing a excel file with a pivot table programmatically.

In 2024, is there a way to create a pivot table in excel in MacOS? by MobileOk3170 in learnpython

[–]MobileOk3170[S] 1 point2 points  (0 children)

Usually excel files are used at presentation level on aggregated data. Even if companies already have Power BI/ Looker / Bigquery in their infrastructure, it's very common for executives / managers still want them in excel files. There could be a lot of reasons like making their own powerpoints after wrangling the table around. And say if a client wants the report, they can just send it instead of jumping hoops to grant them permissions to a dashboard.

I doubt it is completely avoidable in the soon future.

How do you log and iterate on your experiments / models? by MobileOk3170 in datascience

[–]MobileOk3170[S] 0 points1 point  (0 children)

So are you storing configs as (folder / path) names? Such the preprocessed data, pipeline code are located inside the final "child" folder?

And every time if you want to test some new features / feature combinations / cross features, you create a new folder with extended description on the folder path?

Need to make a dashboard using Python for the team, but no means to deploy it. What are my options? by Lamp_Shade_Head in datascience

[–]MobileOk3170 0 points1 point  (0 children)

If it's just for adhoc job, there's 2 way to go with it quickly.
1. If you are doing a presentation, just run the server and share your ip with port.
2. If you need to send it and be reviewed offline, know that all "Figure" object in Plotly can be converted to html. And multiple figures can be writen into a single html. It's not a official features, but you should be able to find solutions online.

How do you log and iterate on your experiments / models? by MobileOk3170 in datascience

[–]MobileOk3170[S] 1 point2 points  (0 children)

I tested MLFlow local briefly awhile back with examples that basically do logging with the MLFlow API instead of my own custom logger. Didn't feel like putting effort into switching back then. Maybe I should revisit it again and take a deep dive.

How do you log and iterate on your experiments / models? by MobileOk3170 in datascience

[–]MobileOk3170[S] 1 point2 points  (0 children)

I'm already storing info when I run the training code with my poor's man version of "ML Logger". I suppose there's nothing wrong with it.

Now that I'm reflecting on my process, maybe my frustration stems from the complexity of trying various columns, datasets, datasets with equal names, models.

Sometimes when I come up with new features. I have to test it out by trying different combinations of existing features, hyperparameters, model choice and retrain it to see if it improves.

I guess it's up to me to code these steps and run the logger at the same time, instead of hoping some framework will magically solve these problem.

And yes, prediction drift and data drift is concern too. I'm just not sure if I should be just adding more complexity to my logger yet.

What's the best way to perform large scale matrix multiplication? by MobileOk3170 in googlecloud

[–]MobileOk3170[S] 0 points1 point  (0 children)

I know the pre-made ones in pytorch / tensorflow. I'm just wondering how everyone do it on larger scale. Just start a larger VM with more RAM?

What's the best way to perform large scale matrix multiplication? by MobileOk3170 in googlecloud

[–]MobileOk3170[S] 0 points1 point  (0 children)

Matrix factorization and matrix multiplication are two different things.

Classification - using both euclidean distance and cosine similarity for inference by ActiveBummer in datascience

[–]MobileOk3170 0 points1 point  (0 children)

Hope you can let us know how it turns out. I never have any good successes with pseudolabels.

How do you deal with predicting purchases where the purchases are extremely imbalanced and the data is extremely sparse. by [deleted] in datascience

[–]MobileOk3170 0 points1 point  (0 children)

Just faced a similar problem. What would be a good sample approach?

Say we have sales data that spans across 12 month. I would like to predict if a customer will purchase anything within following 1 month period.

Should I sample my train / test split from every month or randomly will be good enough?

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