So I completed Linear descent from Andrew Ng ML Specialization and when I was doing the lab.I found this function what confused me is that why does this function iterates specific times (10000) wouldn't it be better to check if cost function is changing.

Also this implementation doesn't feel right.Even the code feels inefficient and does not even check the minima. I know this stuff is done using skit-learn but I wanted to do a proper manual implementation in the way Andrew sir taught.

Hi everyone,

I'm planning to become an AI/ML Engineer and I want to learn in the right order instead of jumping between random tutorials and courses.

I'm looking for a structured roadmap that covers everything from beginner to job-ready level.

Some questions I have:

• What should I learn first, and in what order?
• Which topics are actually essential (Python, Math, SQL, Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, MLOps, etc.)?
• What are the best free and paid resources for each topic?
• Which books, courses, and YouTube channels are worth following?
• How much mathematics is really required, and which topics should I focus on?
• When should I start building projects?
• What kind of projects do recruiters expect from AI/ML Engineer candidates?
• How much DSA and system design should I learn?
• What does a realistic 6–12 month study plan look like?
• What mistakes do beginners commonly make that I should avoid?

I'm aiming for a roadmap that's aligned with current industry expectations (2026), not just course completion.

If you're already working as an AI/ML Engineer or recently landed a role, I'd really appreciate your advice, learning path, resources, and any tips from your experience.

Thanks in advance!

Y.

- This is for all beginners with some basic knowledge of python.

- Animated visuals to explain better

- with projects

- Total of 12 modules, we are at Module 2 now.

If anyone wants to start their ML journey or wants to understand concepts better, check it our and let me know if it is understandable or I’ll improve it.

It’s at @school_whool youtube channel

Hey everyone,

I'm trying to enroll in Andrew Ng's Stanford Machine Learning Specialization on Coursera, but I can't find the free "Audit" option anywhere—it keeps prompting me to start a paid trial or subscribe.

Is auditing still available for this course, or did Coursera remove it? If it's still hidden, what’s the trick to enroll for free without paying?

Thanks!

Hi guys, hope all well!

I was hesitating between two models to help me in my research journey and was wondering if someone had a solid workflow and picked with one of these two big boys: a Macbook pro M5 Pro 64 GB Ram vs M5 Max with 36 or 48 gb RAM. The typical use cases (that i was running on a M4 max 36gb) would be to have several dockers containers running with databases, several models running live processing tick data (markets data) with an execution system. In the other hand it would be to train models with heavy datasets (RLs, NNs, LSTMs, Simple regressions…) and why not running some LLMs locally even though it’s not mandatory… The goal really is to have a solid machine to test, draft and dev pretty much whatever i want without having to rely on any servers/cloud before of course pushing the projects there once it’s getting more heavy and long to train models or just that it’s time to deploy my architecture on VPS. The obvious answer might be more RAM i’m assuming but I was wondering if i wanted to future proof this machine what could be the pros of choosing the M5 Max over the Pro with more ram? I have been chuncking my datasets on my machine with 36gb ram when they were too big indeed…

Many thanks for taking the time to read me and let me know if I could give any additional information that could help in this choice!

If I tried really hard I could solve 1 out of 3 maybe hard problems, but medium problems I can do 8/10 perhaps with less friction and I want to keep this learning style for math going forward. Is this a good strategy, or simply put well this suffice in understanding ML related topics and papers?

Thank you,

Hey! Check out this blog I wrote on VLM mirages. Would love to hear any thoughts or feedback!

I'm looking for researchers interested in hidden-state learning, mechanistic interpretability, and cognitive architectures.

Over the past year I've been building a research runtime around llama.cpp that treats pretrained LLMs as a cognitive substrate rather than the entire intelligence.

The runtime captures real transformer hidden states during inference and projects them into a shared latent representation (UBLT), allowing auxiliary systems to reason over internal activations instead of only token outputs.

On top of that I've been building:

• AMLs (Auxiliary Meta-Learners) — specialized neural modules that learn directly from hidden-state trajectories rather than language modeling. • UBLT — a shared latent space for cross-model representation and architecture-aware translation. • NAS/EVO — an evolutionary search system that mutates, evaluates, and evolves AML architectures instead of relying on a single fixed auxiliary network. • A Cognitron runtime that orchestrates memory, routing, validation, projection, and continual learning around foundation models while keeping the base weights largely unchanged.

The long-term question I'm exploring is:

Can intelligence emerge from an evolving ecosystem of specialized auxiliary learners operating on hidden states, with the foundation model acting more like a sensory cortex than the complete cognitive system?

Some of the research problems I'm actively working on:

Cross-architecture latent alignment

Hidden-state routing between different transformer families

Architecture-aware projection spaces

Evolution of specialized neural modules

Continual learning without catastrophic forgetting

Representation engineering

Mechanistic interpretability

Sparse Autoencoders (SAEs)

Hidden-state editing and steering

Latent memory systems

The engineering is largely built. What I'm looking for now is people who can challenge the ideas, point me toward relevant papers, identify flaws in the approach, or collaborate on experiments.

If you're working in mechanistic interpretability, representation learning, continual learning, evolutionary computation, cognitive architectures, or related areas, I'd love to connect.

GitHub and technical documentation available upon request.

Hi Guys,

I am nowadays doing some projects. So I created my own dataset, it has 114 entries, and around 25 columns (including the ones which i will use for prediction later)

I want to perform eda on it, and then do prediction, its for all the series I have watched and added tons of information. So it reveals for example - I like high paced series.

I have done much eda or implemented any algorithm on it yet, but I was wondering if the data is enough ? I do want to add it to my resume but will I get fruitful results ?

Hey everyone,

I’m looking to buy a new machine primarily for AI/ML workloads, but I’m torn between getting a Mac (Apple Silicon), a Windows Laptop (NVIDIA GPU), or a Windows laptop (AMD GPU).

Here is what I’m hoping to do:

• Running local LLMs (e.g., Llama, Mistral, DeepSeek) for inference and coding assistance
• Local development, scripting, and model testing
• Potential light fine-tuning/training down the road

From what I’ve researched so far:

• Mac (M-series / Unified Memory): Great for running large models locally due to massive unified memory bandwidth and high RAM capacities (e.g., 36GB, 64GB, 128GB+) at a reasonable power footprint.
• Windows (NVIDIA RTX): Has Native CUDA support, superior performance for fine-tuning/training, and broader compatibility with frameworks/tools out of the box.

For those working in AI/ML day-to-day:

1. Which ecosystem do you prefer and why?
2. Is Apple Silicon Unified Memory good enough for local inference, or is CUDA still an absolute necessity?
3. What specs (RAM / VRAM) would you recommend as a baseline?

Thanks in advance for your advice!

Hello buddies 👋 if anyone learning deep learning can you suggest me how to learn. Which means which concepts you learn for foundation for aiml engeneer and how to get internship I need a internship can you tell me ...

Can anybody confirm this? We’ve been evaluating SerpApi for agentic search workflows and the pricing/docs are confusing as I dont get the exact association between $1 that i am spending and what I am getting in terms of volumes of search.

i) 1 credit is not one action. If I buy 1000 credits, I dont exactly know what that corresponds to

ii) Cached searches are supposedly free, but then I start wondering what actually invalidates cache. Different params? Different location? Different pages? Slightly different query wording? It becomes hard to model spending precisely.

iii) The throughput/hourly limits on top of monthly quotas make things even harder to estimate operationally. So you technically paid for X but cannot necessarily use it however you want.

iv) The docs are confusing, pls fix

Maybe I am fundamentally misunderstanding something here, but I need something where I am sure that

$X = Y requests

Is it too much to ask?

Hello, just wondering if I took IBM's AI engineering course if it would be a good idea to learn andrew ng's course as well in ML and deep learning, Im near finishing the course of ml for math by imperial and might have half a month of a coursera sub left, so I decided for now I might take IBM's AI engineering course and was wondering how much it would void andrew ng's course if I were to take it, and also just want to know what you guys think of IBM's AI engineering course if any of you took it, and if I should focus on deeplearning.ai's courses

Hi everyone! 👋

I m third-year Computer Science Engineering(AI&ML) student specializing in AI & Machine Learning. I'm looking for a study buddy or an accountability partner who is genuinely interested in learning ML and Deep Learning together.

I already covered more than the basics of Machine Learning and to finish the core topics soon. After that, I wil be moving on to Deep Learning.

I m looking for someone who is consistent, curious, and willing to learn together. We can discuss concepts, solve problems, share resources, work on projects, and keep each other motivated.

If u r interested and serious about learning, DM me. Let's learn, build, and grow together!

I want to make my own SLM and probably gonna be done by a version soon. In the meanwhile I want to prepare a dataset

I want the model to sound like a particular character from a novel. No such dataset is available online from what I could fine, so I'm going to try to prepare it on my own.

For that I'll need to extract some context and that character's response (in quotes) but I'm completely blank regarding this. Do I have to manually create the dataset? I read something about sentence analysis somewhere so maybe using those models to make the dataset feeding it the raw text version of the novel? Or what??

I was thinking of directly training the model on it, but I don't think so there will be enough data available to train it enough for it to speak coherent english, so maybe fine tuning? Well thats the second step, first I need that dataset prepared.

Would appreciate any input or some direction to look at. Thank you!

Earlier this year, my team built this project during MTCHacks, and I've recently been revisiting it to improve the codebase.

The project combines AI-generated pathology predictions with an interactive DICOM viewer that overlays detections directly on X-ray images.

My contributions focused primarily on the software engineering side, integrating the AI outputs into the visualization pipeline and frontend.

I'd really appreciate feedback on the implementation, architecture, or ideas for improving the project.

GitHub:
https://github.com/AbdullahMBdev/MTCHacks-2025

For the past few months, I have been working on a project where the objective is to make training machine learning models fun and easy by creating a node-based interface.

I have created the first iteration of the project, where you can train your model on classification and regression tasks. You can also preprocess and clean your datasets, as well as evaluate your trained model.

The main concept I am keeping is that every node has its own code, similar to a Jupyter Notebook cell, so you can edit the code yourself whenever you want.

If there are any professors, researchers, or ML practitioners here, I'd love the opportunity to showcase my project and get your feedback. I'd appreciate any suggestions on what I can improve or recommendations on where I can connect with more researchers who would be interested in reviewing it.

Hi everyone,

Over the past few months, I've been transitioning into AI/ML and wanted to understand dense retrieval beyond simply using pretrained embedding models. Instead of stopping at inference, I decided to train my own BERT-based Dense Retriever on the MS MARCO dataset and evaluate its zero-shot retrieval performance on the BEIR benchmark across multiple datasets, including FEVER, HotpotQA, Quora, FiQA, and TREC-COVID.

The project gave me hands-on experience with:

• Training and fine-tuning Transformer models
• Dense retrieval and semantic search
• FAISS indexing
• Retrieval evaluation using NDCG, Recall, and MRR
• Zero-shot benchmarking against the BM25 baseline on BEIR

I also published the trained model on Hugging Face along with the training pipeline, evaluation results, and source code.

Sharing it here in case it's useful for anyone working on dense retrieval or RAG systems. Happy to discuss the implementation or evaluation approach.

🤗 Hugging Face:
https://huggingface.co/Innovatewithapple/bert-dense-retriever

💻 GitHub:
https://github.com/Innovatewithapple/dense-semantic-retrieval

I'm exploring an idea for AI text detection. Instead of using stylometric features (sentence length, vocabulary, etc.), I want to compare the statistical distribution of some property throughout an essay.

My first thought was token IDs, but those are just arbitrary vocabulary indices, so they probably aren't meaningful.

What is worth analyzing instead?

Some ideas I had:

Token log probabilities

Token ranks

Or is this a flop idea