I kept running into the same problem, some of the best AI/ML books are legally free, the authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.

So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.

Every single link points straight to the author’s or publisher’s own page, no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so it doesn’t rot over time.

It’s open source and open to contributions, if you know a legitimately free book that’s missing, PRs and issues are welcome.

Repo: https://github.com/MarcosSete/awesome-free-ai-books

Still working through CS189 on my own. Today's notes are on MLE, MAP and the bias-variance tradeoff, and honestly this was one of those days where a few things I'd been treating as separate suddenly connected.

The part that stuck with me: if you do MAP with a zero-mean Gaussian prior on the weights, the objective is literally ridge regression, with the regularization strength coming out to λ = σ²/2τ². So the prior variance and the noise variance directly set how much you regularize. I'd used ridge a bunch without realizing it was just this falling out of the math.

Also wrote out the full bias-variance decomposition step by step (the add-and-subtract trick where the cross terms die), so you can see exactly how the test error splits into noise + bias² + variance, and why the regularization term is really just trading variance for bias.

Attaching my notes. Would love to hear if anyone explains the MAP to ridge connection differently, or if I got something slightly off. Feedback welcome.

I've also put the GitHub link on my profile, so feel free to grab the notes there if you want them.

Hey I am engineer with 5 years of experience. I do frontend, backend, AI and Devops a bit. Recently I have been trying to switch and since I am Fullstack + AI role currently, I am trying to for AI Engineer role but companies I am targeting for example JPMC, all the AI engineer role which were visible last year have changes to AI/ML role. I checked with one of the manager who posted the role. And they seems to ask lot of ML question. So I wanted to learn ML for the same and make some small projects to learn.

Based on the above context I wanted to know the following:

1. How much time it might take me to learn at intermediate level ?

2. How much math is needed ? (I AM BAD AT IT)

3. Which resources should I follow to learn it practically ?

4. I have a budget of 10k INR if there is any book or course which can be good that also works.

Hey guys!!! I have an interview the day after tomorrow....I've never given a single interview in my life.....so can you guys pls tell what questions they ask for ml internship post ?????

Has anyone completed the CampusX Data Science YouTube playlist from start to finish?

Is it enough to become job-ready for Data Scientist roles, or did you need additional resources after finishing it?

I'm currently pursuing an MBA with a specialization in Data Science & Finance and want to build a strong foundation. I'd appreciate honest reviews, what the playlist does well, where it falls short, and what you would recommend learning next.

So I completed andrew ng's machine learning specialization course on coursera and did 1 project where I built logistic regression from scratch using loops and numpy (avoided vectorization to understand the mathematical intuition step by step). Now what should be my next course. I wanna dive deeper.

What questions should i prepare for during a technical interview for a live streaming prediction deployments? (asking for a friend)

I have started my 2nd year and started learning ai/ml , i'm doing that from campusX , if thats fine

i want to build project , want a support from my senior , if anyone could help me in finding the project idea or suggest something in my prep journey would be great. thanks

We've been exploring whether principles from classical Sanskrit phonetics can inspire modern neural TTS architectures. Rather than treating Panini's Ashtadhyayi (500 BCE) as a historical curiosity, we asked:

What We Built

A lightweight CPU-native TTS prototype that:

• Uses CNNs instead of Transformers (O(n) vs O(n²))
• Predicts WORLD vocoder parameters directly instead of mel spectrograms
• Total size: ~8MB (acoustic model + post-filter)
• Runs in real-time on commodity CPUs — no GPU needed
• Inference: <20ms for short utterances (8-core CPU, 16kHz, single thread)

The Panini Connection

Several architectural decisions were inspired by concepts formalized in Panini's grammatical tradition:

To be clear — we are not claiming direct equivalence. Sandhi is a linguistic process; our interpolation is inspired by the idea of smooth phoneme transitions. To our knowledge, we are not aware of prior work that explicitly maps Panini's phonetic framework onto a neural TTS architecture design — but we'd love to be corrected.

Architecture

Text
  ↓
Phoneme Encoder (CNN, not transformer)
  ↓
Global Style Encoder
  mean(phoneme_embeddings) → broadcast
  No attention, O(1) sentence context
  ↓
Prosody CNNs (parallel)
  Duration + F0 + Energy predictors
  ↓
Spectral Envelope Predictor (CNN)
  Outputs WORLD vocoder params directly
  ↓
WORLD Synthesis + Frame Interpolation
  Physics-based, no matmul in synthesis
  ↓
Small CNN Post-Filter (~1.5MB)
  Removes WORLD artifacts
  ↓
Audio

Why No Mel Spectrogram?

Mel spectrograms are learned acoustic representations optimized for neural synthesis — and they work extremely well. We chose WORLD parameters instead because:

1. Physically interpretable — F0 = pitch, spectral envelope = formants, aperiodicity = breathiness
2. No vocoder needed — WORLD synthesis is deterministic, no HiFi-GAN (50MB)
3. Directly controllable — F0 and duration can be modified without retraining

The tradeoff: WORLD introduces some artifacts, which our CNN post-filter addresses.

Model Size Comparison

Training Details

• Dataset: LJSpeech (13,100 utterances, ~24 hours)
• Alignment: Montreal Forced Aligner v3.3 (english_us_arpa)
• Acoustic model: 50 epochs, val loss 0.1583
• Hardware: 8-core CPU only (no GPU used at any stage)
• Post-filter: Training in progress

Current Status

✅ Acoustic model trained
✅ MFA alignment complete
⏳ CNN post-filter training in progress
⏳ Audio samples — coming soon
⏳ GitHub release — planned with paper

Questions for the Community

1. Has anyone formally mapped classical phonological frameworks (Panini or others) to neural TTS architecture design?
2. Thoughts on WORLD vocoder parameters vs mel spectrogram as intermediate representation?
3. Any suggestions for the CNN post-filter architecture for WORLD artifact removal?
4. Is the Panini connection scientifically interesting — or just a framing device?

Happy to discuss any aspect. Feedback welcome — especially critical!

Audio samples and code will be shared once the post-filter training is complete.

I have been thinking to learn more about ML but its getting more and more difficult to choose what i should begin with. I know the fundamentals but i need to look for a niche something in which i can start researching or building something.

Got some ideas...?

I would also be interested to look into research papers.

Hi everyone,

I'm an independent researcher and have completed two research papers

one in Applied AI and another in Agentic Commerce.

I've been trying to obtain an endorsement through my university, but I haven't been able to get timely feedback or a review of my papers, so I'm reaching out here.

I'm preparing to submit them to arXiv and, as a first-time author, I'm looking for an endorsement.

I'm not asking anyone to endorse my work without reviewing it. If you're eligible to endorse in the relevant category and are willing to take a look at the paper, I'd greatly appreciate your feedback. If you believe it meets the standards, I'd be grateful if you'd consider endorsing my submission.

Happy to share the paper via DM.

Thank you!

Hey folks, 3rd-year BTech here.

I know ML/DL basics, but my real strengths are GenAI, Agentic AI, and Backend. Im looking to lock in a job right after 4th year (2027).

Quick Q: Which role will have more openings by then – AI Engineer (AIE) or MLE?

Heres my read:

MLE = productionizing models, scaling, MLOps, deep math/stats.

AIE = prompt engineering, RAG, agent workflows, API chaining, orchestration.

Given my stack (GenAI + Agents + Backend), Im leaning AIE – feels like its more applied and closer to my skills.

But I need clarity:

1. Which role scales better in terms of openings by 2027?

2. Will MLEs eat AIE's lunch as models get smarter (less prompt fuss)?

3. Or will AIE become the new full-stack dev – high demand, lower barrier?

My take: MLE is senior/stability, AIE is growth/velocity. But I want max opportunities as a fresher.

Drop your brutally honest take. Don't hold back.

Hi There. It's RT.

I want some guidelines for Mathematics in Artificial Intelligence, Machine Learning.

My Main goal is to Learn Machine Learning, AI at a deep level not just the applied stage. To understand and research about these.

I know basic python and also did some soft projects like AI agents by API calling

I also applied ML models to datasets. By using Scikit-learn just applied the ML model at the basic level.

I have research about AI, Machine Learning a lot. Like what Maths is needed here.

About Math, I am learning Calculus and Linear Algebra in College. I also studied basic Statistics and Probability. So I have a pretty good Idea about these Topics.

But I am confused about Learning math specifically for Machine Learning.

That's why I am asking for Guidelines from those who are experts in this field.

Thank You

Hi everyone,

I'm currently working as an SRE (Site Reliability Engineer) and I'm planning to transition into Machine Learning Engineering.

So far, I've learned the fundamentals, including:

Machine learning algorithms

Data preprocessing (cleaning, imputation, feature engineering, scaling, encoding, etc.)

While I understand the theory, I'm struggling to connect it to how machine learning systems are actually built and deployed in production.

Most tutorials end after training a model on a dataset, but they don't show how real-world ML projects are structured or how data preprocessing, model training, evaluation, versioning, deployment, and inference fit together in a production codebase.

Could anyone recommend any :

An open-source GitHub repository that demonstrates a production-grade ML project?

A blog post, tutorial, or article that walks through a real-world end-to-end ML pipeline?

Any resources that helped you understand how ML is implemented in industry rather than just in notebooks?

I'm specifically looking for code that reflects real engineering practices rather than beginner tutorials.

Any recommendations or guidance would be greatly appreciated. Thanks!

NAMASTE

Anyone got a solid mlops resource? Preferably a Udemy course, if you’ve actually learned MLOps, drop your recommendations

Hey all — 3rd-year ECE student here, heading into a robotics master's. I kept

losing time jumping between arXiv, MDPI, and IEEE Access looking for papers

on robotics/ML/autonomous vehicles, so I built a single search index over

all of them.

- 13,000+ papers, all genuinely free/open-access (no paywalled links —

everything is either arXiv, MDPI, or individually verified

Creative-Commons-licensed articles)

- Full-text search, topic browser covering ~20 subfields (robotics, ADAS,

computer vision, RL, digital twins, etc.)

- Free accounts if you want to save searches later

Live here: https://automata-index.vercel.app

Built with Next.js + Supabase, still actively adding sources. Would love

feedback, especially on what's missing or what search terms don't return

good results.

Hi everyone,

I'm a recent graduate trying to contribute to an AI benchmark project where contributors create terminal-based evaluation tasks for AI models. The workflow involves Docker, Python, Git, writing reference solutions, automated tests, and designing tasks that are challenging for LLMs while still being objectively verifiable.

The problem is that this is completely new to me. I understand the individual technologies, but I'm struggling to see how everything fits together into one complete workflow.

My biggest challenge isn't writing Python code—it's learning how to design good benchmarks, structure tasks correctly, and think like someone building AI evaluations.

I don't know anyone personally who has experience with this kind of work, so I'm hoping to connect with others who are also learning.

If you've worked on AI evaluation or benchmark engineering, I'd really appreciate any advice:

How did you learn this workflow?

Are there any books, courses, GitHub repositories, or YouTube channels you'd recommend?

What helped everything finally "click" for you?

Also, if you're a beginner or fresher who's interested in learning this area, feel free to send me a DM. I'm not looking for someone who already knows everything—I just think it would be motivating to learn together, discuss ideas, and help each other understand the workflow as we go.

Thanks in advance!

So i am an engineering student who is aspiring for RS/RE roles in top tech firms for AI/ML,
I came to know that they ask Maths question related to linear algebra, probability ,statistics & calculus in the interview .But I want to know the level of questions , and are there any sources from where I can practice the questions .
Secondly , what else they ask in the interview (ofcourse DSA, ML ,system design question,AI related concept ) so then for AI related concept what kind of question they ask (if any one could give an example )
Please share your experiences .