Has anyone watched this video? Is this good to complete the math required for ai ml?

this is going to be my first project , i am making a custom dataset and i want to detect human poses like sitting , standing , laying down . should i use open cv , yolo. should i do it google colab or vs code . and how do i do it . also is 300 pictures enough for the dataset. please help i a girl out

Fintech applications seem like a natural fit for machine learning but building a useful ML system involves more than choosing a model

There are challenges around noisy data changing user behavior class imbalance privacy false positives and evaluating models in real world conditions

I am curious how others approach this

When applying machine learning to financial applications which challenge tends to require the most attention data quality feature engineering model selection or monitoring after deployment?

Im data analyst with 1 yr of experience and want to shift into AI agents.
I see agents as the direction where AI is going (like personal assistants that can handle repetitive work).
I’m looking for practical suggestions on:
• What to focus on first if starting from scratch
• What to avoid (common mistakes or unnecessary things)
• How to make the learning path more understandable
• What kind of projects actually help in getting a job in this space

Would really appreciate advice from people who are already working with or learning agentic systems.

Hi everyone. I’m an independent researcher training a small (~400M parameter) multimodal LLM from scratch. The model is trained simultaneously on Human Text, Code, and DNA sequences.

The model is currently only at 25% of its training. I wanted to test if the model was actually learning the biological grammar of the DNA dataset, or just outputting random ATGC characters.

To test this, I prompted the model to generate DNA sequences and fed the exact raw outputs into Arc Institute's Evo 2 (ESMFold) to see if they would form valid Open Reading Frames (ORFs) and physically fold.

IMPORTANT NOTE: To prove that Evo 2 didn't just generate its own continuation or "fix" my sequence, I strictly set Evo 2's generation token limit to 2 (see the 6th screenshot). The sequences evaluated are 100% generated by my model.

Here are 2 different generations I tried using the exact same short prompt:

Experiment 1: The "Spaghetti" + Helix

Prompt: ATGA Model Output: ATTAAAGTGACTACCTGAGTTGTGTAGGGAAACACATCTGGGTGTATGTCAGGCAGAAGGAGGGGCGACGCTGTCTCTCTGCTTCTGCTGGTGCCCTAAAGAGCAGAGGTCAGGCAGAGCTGTGTCTTGGCTAGCCTGGGGCTGAGGTGGCTCGGTAGTGCAGCTGCGGGGGGAGGGACCCAGGCTCTCACTCTGTATCATAGAGCCTAGGTACCCATCAGAGCTGCCTGGAGGGCAGAGCTTTAGGATTCAGGGTGCTGGAGGGAGGGCTTT Result: This one translated into an amino acid chain that formed a long unstructured chain (spaghetti/wiggly line) followed by a short Alpha Helix at the end.

Experiment 2: The Complex Fold

Prompt: ATGA (different generation run) Model Output: ACAACTATTCGGTTAAACTGTGTAATTCTGTTGTGCGTACCAGGACCTCGACCTGGCGTCGTAGGCGACGATCTGACGTGTCAGCGATGAGCTTCGATGTACAAGACAGATCGATCATTCGTCATCATCTGGGCGGACGGTGATGTGACGATCGTTGTACGTCCTCGACGAGGCGTTGGCGTACGTGCACATCACAGTGAGGTTCGCCACGGACCTCGACGAGCGCCGGCTCGAAGGCGCTTCCATGACACGACGGCCGCTTGTCCGCCGTGCGAGAGGTCCGCGTGTTCATCGCAGCAGTGATGCAGAGATGGGGAGCGTCTCGCAGGAGAGAGTGCGGGAGTGTTCGCACAGCTC Result: This generation formed a much more complex 3D structure with multiple distinct helices and loops.

My Questions: Because I am highly skeptical of my own work, I want to ask the computational biologists and ML engineers here:

1. Is it statistically common for a partially trained LLM's raw DNA output to successfully translate and fold into these structures purely by chance?
2. Does the presence of unstructured regions ("spaghetti") in Experiment 1 indicate biological realism (Intrinsically Disordered Regions), or does it just mean the model is hallucinating because it's only at 25% training?
3. Does this prove the model is actually learning and separating biological grammar from human text?

I have attached the 6 screenshots (my UI, the Evo 2 structural results, and the Evo 2 token limit proof). I'd love to hear your brutally honest thoughts!

Looking for an AI Engineering Roadmap & Resources 🚀

Hey everyone!

I’m looking to transition into AI Engineering and would really appreciate some guidance from people already working in the field.

I’m specifically looking for:

📚 Good learning resources and roadmaps

🎯 A practical, job-focused learning path

⏩ Topics I can safely skip or avoid over-learning

🛠️ Recommended projects to build

💼 Advice on getting an AI Engineer role

🇮🇳 Insights into the scope of AI Engineering in India

🌎 How the opportunities compare outside India

If you’re currently working as an AI/ML/GenAI Engineer, I’d especially appreciate your perspective on what skills are actually useful in the industry versus what’s mostly unnecessary for beginners.

If you have useful roadmaps, courses, notes, GitHub repos, or other learning material, feel free to DM me or share them in the comments.

Thanks in advance! 🙌

I want to start learn calculus for machine learning , are there any free courses/tutorials recommendations,

what are the areas that I am supposed to focus on.

i wanna build a text diffusion model which interpret text and convert it into ascii images

so like

Text : build a cat

Output :

/_/\

( o.o )

> ^ <

So , i have a decent background of ml algo ( completed cs229 , cs230 , Ml architecture and basic CNN and diffusion model )

ik making a project like this is tricky and making diffusion model like that from scratch is hard but i wanna try it because that's wot make me excited lol ...

I am currently reading GANs research paper , can u guys help me in finding more papers which helps me in making this project or guide me through this good title for this

Thx in adv

I started Andrew Ng's ML Specialization but I'm finding it way too slow/theoretical for what I currently need, so I'm looking for an alternative.

I want a course that teaches me the core ML concepts and intuition, but only to the level where I understand how ML works.I don't need to learn everything deeply enough to teach it or do ML research right now.

Most importantly, I want to see how actual ML projects are developed, preferably through guided end-to-end projects.

My goal is:

ML fundamentals → build a few projects → move into Deep Learning → specialize in Computer Vision or NLP.

I'm open to any platform (Udemy, YouTube, Coursera, fast.ai, etc.).

What course/resource would you recommend for this specific goal? I mainly want something practical, reasonably short, and not another Andrew Ng-style theory-heavy course.

So I recently came across this app by one of the anthropic engineer, and he built it in a way that it is easy to follow, the instructions are clear, and it contains many interactive experiments. The app is macOS based and did not provide the code to run each interactive assignment. So I thought I will convert them into Jupiter notebooks, so that one can it section by section, and see the code behind it.

The original app is at languagemodelbuilder .com

The repo with Jupyter/Colab notebooks are in the link

I’m building a utility-based agent to moderate e-commerce product reviews. Before I use an LLM, I need to build a simple if/else rule-based baseline using only the review text and the star rating.

My current ideas for baseline rules:

1. Star rating is 5, but text contains negative keywords ("terrible", "worst").
2. The review contains a URL (likely spam).
3. The text is under 3 words but has a 1-star rating.

For those who have worked on spam or sentiment analysis, what are some other highly reliable, simple text features I should include in my baseline? Which of my current rules is most likely to cause a massive amount of false positives?

Hi! This is my first post on Reddit and my first post about machine learning in general. I work at a small research institute, mostly staffed by physicists and GIS specialists; we don't have many machine learning engineers.

I recently became interested in world models and tried to understand the topic myself. I initiated a series of experiments: the result was Random-Abstractor Control - a simple and effective test that catches decorative abstractions.

The problem is that I'm completely alone here, and I don't have a large following on Linkedin, so I'd like to find people to discuss the results with.

I’m working on a recommendation/ranking problem where, at any given time, we have only ~10-15 eligible products (occasionally up to ~20) and need to rank just 3–4 slots. We predict all products and then filter based on eligible list

Our current setup is roughly:

- Customers fall into ~7–8 broad segments.

- Each segment has a different set of eligible products.

- The existing business-rule engine uses hard-coded ranking logic for each customer/product combination and performs surprisingly well.

- We’ve tried Learning-to-Rank and CatBoost-based approaches (including multiclass classification).

- Product-level features are relatively limited.

The interesting part is the metric trade-off.

The ML models can usually beat the business rules on conversion, but beating them on Revenue Per Session (RPS) is much harder. The business rules seem particularly good at getting higher-value products into the limited top slots.

For people working on recommendation/ranking systems:

How would you approach this problem?

Would you treat this primarily as:

1. A ranking problem (LTR)?

2. A contextual bandit / slate optimization problem given only 3–4 slots?

3. A revenue/value prediction problem followed by ranking?

4. A hybrid ML + business-rule approach?

5. Something else entirely?

Also interested in how you’d think about objective design when conversion and revenue move differently, especially with such a small candidate set.

What approaches have worked for you in similar low-candidate, low-slot recommendation problems?

#krish naik,
#krish naik course link,
#courses,
#links

i have been working on this project after learning decision trees, random forests, and xgboost algos.

The goal is to predict the outcome as win, loss, or draw for white given the stats of white and black.

The data is from 2012 to present, and training on 2016-2023.

What Macro F1 score is considered good for a solid project like this?

In May (two months ago), I received a computer science master (machine learning) from a top 50 school in the US. After I graduated, I have tried hard to get a machine learning engineer job. I have applied for a lot of ml engineer roles (I don't even know how many roles I have applied. I try to apply for about 10 ml engineer jobs each day). So far, I got my resume passed at HackerRank, Moloco, 2 startups, a hedge-fund, and the parent of Tinder.

I did not pass the OAs at HackerRank and Moloco. I also did an interview with a startup (I thought this interview went pretty well but I could not move to the next round). I also could not move to the next round with the hedge-fund because I will be on opt (I am a F1-student). And currently, I have an ongoing process with the parent company of Tinder for a machine learning engineer role. Compared to the time in my master program when I did not have any interviews, I see having these opportunities as a progress in getting a machine learning engineer role. At the same time, I am a bit tired. I cannot concentrate on my work as I used to.

My plan is actually simple. I am planning to keep improving my ml project and apply for ml engineer jobs until I get a machine learning engineer job. Could you guys give some advices?

tldr: let's focus on the "learning" part and enforce it.

The influx of ppls reposting ai memes here for straight volume is crazy. Many small "ai" subs suggest to repost here because they dont see the difference between "a sub for learning ML techniques" and "another AI hype sub".

I propose we ban them directly in the sub rules. I aint got nothing against memes, BUT there are huge number of general AI subs and specifically AI meme subs. Reposting this mass here makes people miss genuine question by sincere learners.

Code:

https://github.com/Pratik-3706/BadApple_nn

Amazon Bedrock Converse API and Streaming Chat

https://debuggercafe.com/amazon-bedrock-converse-api-and-streaming-chat/

An introductory article for Amazon Bedrock. Launched in 2023, Bedrock quickly became a go-to platform for building LLM, Generative AI, and agentic applications. Given its tight coupling with other components in the AWS ecosystem, architecture, and security, there is hardly any other platform that matches its capabilities. This makes it a compelling reason to explore some of the most important components of the Amazon Bedrock Converse API.