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// Auto-generated by build.js — do not edit manually.
// Last built: 2026-09-27T05:58:16.242Z
// Last built: 2026-09-27T10:09:58.491Z
const ROADMAP_PREREQS = {
"0": [],
@@ -920,7 +920,7 @@ const PHASES = [
"lang": "Python",
"url": "https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/phases/04-computer-vision/26-monocular-depth/",
"summary": "A depth map is a single-channel image where each pixel is a distance from the camera. Predicting it from one RGB frame used to be impossible without stereo or LiDAR. In 2026 a f…",
"keywords": "Relative vs metric depth · The encoder-decoder pattern · Why a single image produces depth at all · What monocular depth cannot do · Depth Anything V3 in 2026 · Marigold — diffusion for depth · Intrinsics and the pinhole camera · Evaluation · Step 1: Depth metrics · Step 2: Scale-and-shift alignment · Step 3: Lift depth to a point cloud · Step 4: Smoke test with a synthetic depth scene · Step 5: Depth Anything V3 usage (reference)"
"keywords": "Relative vs metric depth · The encoder-decoder pattern · Why a single image produces depth at all · What monocular depth cannot do · Depth Anything V3 in 2026 · Marigold — diffusion for depth · Intrinsics and the pinhole camera · Evaluation · Step 1: Depth metrics · Step 2: Scale-and-shift alignment · Step 3: Lift depth to a point cloud · Step 4: Smoke test with a synthetic depth scene · Step 5: Depth Anything V2 usage (reference)"
},
{
"name": "Multi-Object Tracking & Video Memory",
@@ -1242,7 +1242,7 @@ const PHASES = [
"lang": "Python",
"url": "https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/phases/06-speech-and-audio/03-audio-classification",
"summary": "Everything from \"dog barking vs siren\" to \"which language is this\" is audio classification. The features are mels. The architecture moves each decade. The evaluation stays AUC, …",
"keywords": "Class imbalance is the real challenge · Evaluation · Step 1: featurize · Step 2: fixed-length summary · Step 3: k-NN · Step 4: upgrade to CNN on log-mels · Step 5: the 2026 default — fine-tune BEATs"
"keywords": "Class imbalance is the real challenge · Evaluation · Step 1: featurize · Step 2: fixed-length summary · Step 3: k-NN · Step 4: upgrade to CNN on log-mels · Step 5: fine-tune a pretrained audio transformer (AST shown)"
},
{
"name": "Speech Recognition (ASR)",
@@ -1885,7 +1885,7 @@ const PHASES = [
"lang": "Python",
"url": "https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/phases/10-llms-from-scratch/11-quantization/",
"summary": "A 70B model in FP16 needs 140GB. Two A100s just for weights. Quantize to FP8: one 80GB GPU. INT4: a MacBook.",
"keywords": "Number Formats: What Each Bit Does · How Quantization Works · Sensitivity Hierarchy · PTQ vs QAT · GPTQ, AWQ, GGUF · Quality Measurement · Real Numbers · Step 1: Number Format Representations · Step 2: Symmetric Quantization (Per-Tensor and Per-Channel) · Step 3: Quality Measurement · Step 4: Bit-Width Sweep · Step 5: Sensitivity Experiment · Step 6: Simulated GPTQ · Step 7: AWQ Simulation · Step 8: Full Pipeline · Quantizing with AutoGPTQ · Quantizing with AutoAWQ · Converting to GGUF · Serving quantized models"
"keywords": "Number Formats: What Each Bit Does · How Quantization Works · Sensitivity Hierarchy · PTQ vs QAT · GPTQ, AWQ, GGUF · Quality Measurement · Real Numbers · Step 1: Number Format Representations · Step 2: Symmetric Quantization (Per-Tensor and Per-Channel) · Step 3: Quality Measurement · Step 4: Bit-Width Sweep · Step 5: Sensitivity Experiment · Step 6: Simulated GPTQ · Step 7: AWQ Simulation · Step 8: Full Pipeline · Quantizing with GPTQModel · Quantizing to AWQ with LLM Compressor · Converting to GGUF · Serving quantized models"
},
{
"name": "Inference Optimization",
@@ -3648,7 +3648,7 @@ const PHASES = [
"lang": "Python",
"url": "https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/phases/17-infrastructure-and-production/04-vllm-serving-internals/",
"summary": "Modern serving-engine throughput rests on three compounding defaults, not a single trick. PagedAttention is always on. Continuous batching injects new requests into the active b…",
"keywords": "PagedAttention as a virtual memory system · Continuous batching at the iteration level · Chunked prefill protects TTFT tail · The three defaults interact · The 2026 v0.18.0 gotcha · Numbers you should remember · What the scheduler looks like"
"keywords": "PagedAttention as a virtual memory system · Continuous batching at the iteration level · Chunked prefill protects TTFT tail · The three defaults interact · Check the compatibility matrix · Numbers you should remember · What the scheduler looks like"
},
{
"name": "EAGLE-3 Speculative Decoding in Production",