Learn AI &amp; ML the Easy way

Gen AI

Transformers, LLMs, RAG, Agents, and the generative AI stack

Core ML

Classical ML, deep learning, training patterns, and foundations

Data Concepts

Data engineering, pipelines, and data-centric AI

Math

Linear algebra, calculus, probability for ML

Definitions

Reinforcement Learning

A paradigm of machine learning where an agent learns to make sequential decisions in an environment by performing actions and receiving numerical rewards.

Analogy: Like training a dog by giving it a treat when it successfully fetches a ball, and ignoring it when it completely misses the mark.

Related: PPO, Reward Model

Ablation Study

A systematic evaluation process where individual components of a machine learning model are removed to measure their exact contribution to performance.

Analogy: Like removing ingredients from a recipe one by one to figure out exactly which spice is making the soup taste good.

Activation Function

A mathematical operation applied to a neural network node's output that introduces the crucial non-linearity required to learn highly complex patterns.

Analogy: Like a gatekeeper deciding how loudly a message should be shouted to the next room based on the importance of the incoming signal.

Related: SwiGLU, Softmax, Adapter

Activation Patching

An interpretability technique that proves a network component handles a specific concept by pasting its clean activations into a corrupted forward pass and w...

Analogy: Like bypassing a broken house fuse with a battery to prove that specific fuse controls the kitchen lights.

Read the full concept

Active Learning

A specialized training paradigm where the model explicitly selects the most informative unlabelled data points and queries a human annotator for labels.

Analogy: Like a student who only asks the teacher for help on the hardest questions they don't understand, rather than asking about everything in the textbook.

Papers

Attention Is All You Need

The landmark 2017 paper that introduced the Transformer architecture, replacing RNNs with self-attention and launching the modern era of large language models.

Are Emergent Abilities a Mirage?

Are Emergent Abilities of Large Language Models a Mirage?

A powerful rebuttal arguing that the 'sudden' appearance of capabilities in LLMs is merely a statistical illusion caused by researchers choosing non-linear, discontinuous metrics.

AWQ

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Introduced Activation-aware Weight Quantization, a method that preserves LLM performance by identifying and protecting a tiny fraction of highly salient weights based on activation magnitudes.

BERT

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Introduced bidirectional pretraining by masked language modelling, setting a new standard for natural language understanding tasks.

Better & Faster LLMs via Multi-token Prediction

Better & Faster Large Language Models via Multi-token Prediction

Proposed training LLMs to predict the next $N$ tokens simultaneously rather than just the single next token, improving reasoning capabilities and creating a built-in draft model for fast speculative decoding.

Interview Questions

"How do multimodal LLMs process and reason over text, images, audio, or video?"

Quick answer

Multimodal LLMs project disparate data types (like pixels and audio waveforms) into a shared continuous embedding space, allowing a single transformer architecture to process them alongside text tokens.

"How do you implement data privacy and PII protection in a Generative AI application?"

Quick answer

By employing data masking/redaction before sending prompts to the LLM, using zero-data retention policies with model providers, or deploying open-weights models locally to ensure data never leaves the corporate boundary.

"How do you monitor and maintain a Generative AI application after deployment, including drift, quality degradation, latency, and safety issues?"

Quick answer

Monitoring requires logging end-to-end traces, tracking cost and latency metrics, tracking explicit user feedback (thumbs up/down), and sampling production data with LLM-as-a-judge pipelines to detect behavioral drift over time.

"How would you build an end-to-end evaluation framework for an LLM application in production?"

Quick answer

An evaluation framework requires a curated golden dataset, automated LLM-as-a-judge metrics for assessing relevance and tone, and continuous A/B testing or human-in-the-loop validation for production monitoring.

"How would you design an end-to-end Generative AI application from data ingestion and model selection to deployment, evaluation, monitoring, and continuous improvement?"

Quick answer

An end-to-end design splits into an offline data pipeline for embedding knowledge, an online inference pipeline for generating answers via RAG, and an asynchronous feedback loop for evaluation and continuous tuning.

Cheat Sheets

Generative AI

A practical reference for LLMs, prompting, RAG, agents, fine-tuning, evaluation, and production systems

View cheat sheet

Learning Paths

Machine Learning Engineer

A Learning Path Editor roadmap document.

Open roadmap

Comparisons

Accuracy vs AUC vs F1

Choosing the right metric to evaluate a classification model, especially when your data is highly imbalanced.

Adam vs SGD with Momentum

How modern optimizers navigate the complex loss landscape to find the best model weights.

AI vs Machine Learning vs Deep Learning vs Gen AI

Understanding the nested relationship from general artificial intelligence down to modern generative models.

Aleatoric vs Epistemic Uncertainty

Understanding the difference between the noise in the data and the ignorance of the model.

Bagging vs Boosting vs Stacking

Understanding the three main ensemble learning techniques used to combine multiple models.

Blog

Governance

AI Safety is Mostly Engineering Right Now

AI safety isn't just philosophical debates. It is guardrails, red teaming, and why a system prompt isn't enough.

Cloud Services

AWS SageMaker: When to Use It (And When Not To)

SageMaker is huge, expensive, and powerful. Here is when you actually need it, and when you are just burning money.

Ecosystem

On-Device Inference: Running LLMs on Your Phone

Running LLMs on a phone isn't a gimmick anymore. It is how you fix latency and privacy in one shot.

Model News

The Open-Weight Landscape in 2026

The gap between closed and open-weight models is basically gone. Here is what that means for developers.