100% Free to Learn. Built to Master.

Learn AI & ML the Easy way

Concepts · Playgrounds · Insights · Interview Prep

Playgrounds

Modern ML

Attention

Focus a token and watch how strongly it draws on every other one, in the graph and in the matrix at once. Raise the temperature until it stops choosing at all.

Foundations

Bias-Variance Tradeoff

Sweep a complexity slider from a flat underfit to a wild overfit, and watch what happens to train and validation errors. Resample to see variance in action.

Evaluation & RL

Threshold, Confusion Matrix & ROC

Drag one threshold and watch the confusion matrix, precision, recall and the ROC point all move together. Then push it to either extreme and see one metric go perfect while the other collapses.

Foundations

Gradient Descent

Drag the data, drag the line, then watch an optimiser find the fit for you — one real gradient step at a time. Push the learning rate too far and watch it fly apart.

Foundations

The dataset renders as a row of small tiles. A split-ratio slider animates tiles sliding apart into labelled train and test bins. A k-fold toggle reshapes the row into k blocks; the validation block advances one position per step with a smooth slide, each fold appending a score bar to a chart on the right, ending in a mean ± std readout. Shuffle and stratify toggles reorder the tiles visibly.

Concepts

View all

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

Papers

View all

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.

Read breakdown

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.

Read breakdown

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.

Read breakdown

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.

Read breakdown

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.

Read breakdown

Interview Questions

View all

Generative AI

"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.

Generative AI

"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.

Generative AI

"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.

Generative AI

"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.

Generative AI

"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.