> For the complete documentation index, see [llms.txt](https://nishchalnishant.gitbook.io/artificial-intelligence/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nishchalnishant.gitbook.io/artificial-intelligence/readme.md).

# Welcome

You already ship software: pipelines, environments, rollouts, observability. **Machine learning is that same muscle** — except the "binary" you deploy is a model, and the bugs can drift in silently like a change in crowd noise at Wankhede.

This repo is your **two-track studio album**: fast interview-ready takes, and deeper "liner notes" when you want the full mix.

***

## Repo Structure

```
01-foundations/          Intro to AI, glossary, revision guide
02-classical-ml/         Supervised, unsupervised, preprocessing, anomaly detection, active learning, Bayesian methods
03-deep-learning/        Components + methods + transfer learning + video + 3D vision + PyTorch
04-specialized-domains/  RL, RecSys, GNNs
05-llms/                 Architecture, training, applications, interview notes
06-production-ml/        MLOps, system design
07-interview-prep/       ML + LLM interview Q&As, scenarios, coding
08-emerging-topics/      Emerging trends, XAI, causal inference, privacy-preserving ML, continual learning
09-study-plans/          30-day week-by-week study plan
10-references/           Book notes, research papers, datasets, tools, benchmarks, notation
MIND-MAP.md              Full topic mind map + gap analysis
```

***

## Track 1 — Interview Blueprint (high tempo)

**Where to start tonight**

* [**AI & ML revision guide**](/artificial-intelligence/ai_ml_revision_guide.md) — Golden rules, architectures, math you can whiteboard, infra patterns, gotchas.
* [**30-day roadmap**](/artificial-intelligence/09-study-plans/09-study-plans.md) — Structured reps from zero to "I can hold the room."
* [**Math derivations hub**](/artificial-intelligence/07-interview-prep/math-derivations.md) — Chain rule to attention, with your pen.
* [**Interview hub**](/artificial-intelligence/07-interview-prep/07-interview-prep.md) — canonical entry point for interview prep.
* [**ML interview notes**](/artificial-intelligence/07-interview-prep/ml.md) — full classical + DL Q\&A bank.
* [**LLM deep dives**](/artificial-intelligence/05-llms/interview-notes.md) — RAG, agents, alignment, serving.
* [**Pre-interview checklist**](/artificial-intelligence/07-interview-prep/pre-interview-checklist.md) — 48h → 24h → morning-of: what to review, verify, and do before walking in.

**10-Minute Revision Cards** (skim before any topic or interview)

| Section                               | Revision Card                                                              |
| ------------------------------------- | -------------------------------------------------------------------------- |
| Foundations                           | [REVISION.md](/artificial-intelligence/01-foundations/revision.md)         |
| Classical ML                          | [REVISION.md](/artificial-intelligence/02-classical-ml/revision.md)        |
| Deep Learning                         | [REVISION.md](/artificial-intelligence/03-deep-learning/revision.md)       |
| Specialized Domains (RL, RecSys, GNN) | [REVISION.md](/artificial-intelligence/04-specialized-domains/revision.md) |
| LLMs                                  | [REVISION.md](/artificial-intelligence/05-llms/revision.md)                |
| Production ML                         | [REVISION.md](/artificial-intelligence/06-production-ml/revision.md)       |

**Azure / DevOps bridge:** *Training is your build job; the model artifact is your release candidate; inference is the always-on service; MLOps is CI/CD when the "code" and the "data" both change.*

***

## Track 2 — Deep-Dive Library (studio sessions)

| Section                                                                                                                                       | What's inside                                                  |
| --------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- |
| [Foundations](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/01-foundations/README.md)                                | Intro to AI, glossary, revision guide                          |
| [Classical ML](/artificial-intelligence/02-classical-ml/02-classical-ml.md)                                                                   | Bias–variance, trees, calibration, when classical beats DL     |
| [Deep Learning](/artificial-intelligence/03-deep-learning/03-deep-learning.md)                                                                | Activations, backprop, attention, PyTorch, MCP                 |
| [Computer Vision](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/03-deep-learning/methods/computer-vision.md)         | CNNs, detection, ViT, CLIP, self-supervised                    |
| [Generative Models](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/03-deep-learning/methods/generative-models.md)     | VAE, GAN, DDPM/DDIM, Stable Diffusion, Flow Matching, DiT/FLUX |
| [Time Series](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/03-deep-learning/methods/time-series.md)                 | ARIMA through Transformers, forecasting, anomaly detection     |
| [Reinforcement Learning](/artificial-intelligence/04-specialized-domains/reinforcement-learning.md)                                           | MDPs, Q-learning, PPO, RLHF connection                         |
| [Recommender Systems](/artificial-intelligence/04-specialized-domains/recommender-systems.md)                                                 | Collaborative filtering, two-tower, ranking                    |
| [Graph Neural Networks](/artificial-intelligence/04-specialized-domains/graph-neural-networks.md)                                             | GCN, GraphSAGE, knowledge graphs                               |
| [LLMs](/artificial-intelligence/05-llms/05-llms.md)                                                                                           | Architecture, training, scaling, evaluation                    |
| [LLM Applications](/artificial-intelligence/05-llms/applications.md)                                                                          | RAG, agents, tuning, inference optimization                    |
| [Multimodal AI](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/05-llms/applications/multimodal.md)                    | CLIP, VLMs, fusion architectures, audio, video, deployment     |
| [Speculative Decoding](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/05-llms/applications/speculative-decoding.md)   | Medusa, Eagle, standard SD, production trade-offs              |
| [LLM Training Stability](/artificial-intelligence/05-llms/training-stability.md)                                                              | Loss spikes, mixed precision, RLHF failure modes               |
| [Production ML](/artificial-intelligence/06-production-ml/06-production-ml.md)                                                                | MLOps, CI/CD for ML, deployment                                |
| [Model Governance](/artificial-intelligence/06-production-ml/model-governance.md)                                                             | Model registry, audit trails, GDPR, champion-challenger        |
| [ML System Design](/artificial-intelligence/06-production-ml/system-design.md)                                                                | Design patterns, case studies, engineering                     |
| [Interpretability & XAI](/artificial-intelligence/08-emerging-topics/interpretability-and-xai.md)                                             | SHAP, LIME, mechanistic interpretability                       |
| [Causal Inference](/artificial-intelligence/08-emerging-topics/experimentation-and-causal-inference.md)                                       | A/B testing, causal graphs, uplift modeling                    |
| [Emerging Trends 2023–2025](/artificial-intelligence/08-emerging-topics/emerging-trends.md)                                                   | Mamba, MoE, test-time scaling, synthetic data, long context    |
| [2025 Frontier Models](/artificial-intelligence/08-emerging-topics/2025-frontier-models.md)                                                   | DeepSeek, Llama 4, Gemini 2.5, Claude 3.7, GPT-o3, Qwen3       |
| [Model Compression](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/03-deep-learning/components/model-compression.md)  | Quantization, distillation, pruning                            |
| [Research Papers](/artificial-intelligence/10-references/research-papers.md)                                                                  | 25 foundational LLM papers every ML interview cares about      |
| [Anomaly Detection](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/02-classical-ml/anomaly-detection.md)              | IForest, OCSVM, LOF, Autoencoders                              |
| [Active Learning](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/02-classical-ml/active-learning.md)                  | Uncertainty sampling, QbC, Core-Set, BADGE                     |
| [Bayesian Methods](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/02-classical-ml/bayesian-methods.md)                | GPs, BNNs, variational inference, Bayesian optimization        |
| [Conformal Prediction](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/02-classical-ml/conformal-prediction.md)        | Distribution-free coverage guarantees, CQR                     |
| [Transfer Learning & Domain Adaptation](/artificial-intelligence/03-deep-learning/transfer-learning.md)                                       | Fine-tuning, DANN, few-shot, MAML, zero-shot                   |
| [Video Understanding](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/03-deep-learning/methods/video-understanding.md) | Two-stream, I3D, SlowFast, Video Transformers                  |
| [3D Vision & Point Clouds](https://github.com/nishchalnishant/Ml-overview/blob/modifying-using-ai/03-deep-learning/methods/3d-vision.md)      | PointNet, PointNet++, NeRF, 3DGS, autonomous driving           |
| [Privacy-Preserving ML](/artificial-intelligence/08-emerging-topics/privacy-preserving-ml.md)                                                 | DP-SGD, Federated Learning, SMPC, HE                           |
| [Continual Learning & NAS](/artificial-intelligence/08-emerging-topics/continual-learning.md)                                                 | Catastrophic forgetting, EWC, replay, DARTS                    |

***

## How These Notes Are Written

Each topic aims for a **senior answer in three beats**:

1. **Direct line** — What you say in the first ten seconds.
2. **Intuition** — An analogy that sticks.
3. **Production** — Latency, cost, scale, and what breaks first.

***

> **Cold open:** Production ML is a lot of engineering with a little bit of "magic." This repo leans into the engineering — because that's what keeps models out of the **Incident** channel.