π§ Complete AI Model Building & Services Roadmap
Text Β· Image Β· Video Β· 3D Β· AR/VR/XR β From Zero to Production
PHASE 0 β FOUNDATIONS (Months 1β3)
0.1 Mathematics & Statistics Core
Linear Algebra
- Vectors, Matrices, Tensors (rank, shape, broadcasting)
- Matrix operations: dot product, transpose, inverse, determinant
- Eigenvalues & Eigenvectors (PCA backbone)
- SVD β Singular Value Decomposition (used in compression, recommendation)
- Norms: L1, L2, Frobenius
- Jacobian & Hessian matrices (used in backpropagation)
Calculus
- Partial derivatives, Chain Rule (basis of backprop)
- Gradient, Divergence, Curl
- Taylor Series approximations
- Integral calculus for probability distributions
- Optimization landscapes: saddle points, local minima, global minima
Probability & Statistics
- Probability theory: Bayes' theorem, conditional probability
- Distributions: Gaussian, Bernoulli, Multinomial, Poisson, Beta, Dirichlet
- Maximum Likelihood Estimation (MLE) & MAP
- Information Theory: Entropy, KL Divergence, Cross-Entropy
- Monte Carlo methods, Markov Chains (MCMC)
- Hypothesis testing, p-values, confidence intervals
Optimization Theory
- Gradient Descent (Batch, SGD, Mini-batch)
- Momentum, RMSProp, Adam, AdaGrad, AdamW, LAMB
- Learning rate scheduling: cosine annealing, warmup, cyclic LR
- Lagrange multipliers, constrained optimization
- Convex vs. non-convex optimization
0.2 Programming & Software Stack
Python Mastery
- NumPy: vectorized operations, broadcasting, memory layout
- Pandas: data wrangling, groupby, merge, time series
- Matplotlib / Seaborn / Plotly: visualization pipelines
- Multiprocessing, asyncio, threading for data loading
Deep Learning Frameworks
- PyTorch (primary): autograd, nn.Module, DataLoader, DDP, FSDP
- TensorFlow/Keras: TF2.x, SavedModel, TFLite, TF Serving
- JAX: XLA compilation, pmap, vmap, grad transformations
- Triton: custom GPU kernels (intermediate/advanced)
- ONNX: cross-framework model serialization
MLOps & Infrastructure
- Docker, Kubernetes, Helm charts
- MLflow, Weights & Biases (W&B), Comet, Neptune
- DVC (Data Version Control)
- FastAPI / Flask for serving
- Ray, Dask for distributed computing
- Apache Kafka for streaming data pipelines
0.3 Hardware Foundations
GPU Architecture
- CUDA cores vs. Tensor Cores (A100, H100, RTX 4090)
- GPU memory hierarchy: registers β shared mem β L1/L2 cache β VRAM
- PCIe vs. NVLink bandwidth (critical for multi-GPU training)
- NVIDIA CUDA, cuDNN, cuBLAS, NCCL
- Mixed precision: FP32, FP16, BF16, INT8, INT4, FP8
Hardware Tiers for Different Workloads
| Workload | Minimum | Recommended | Production |
|---|---|---|---|
| Text LLM (7B) | RTX 3090 24GB | A100 40GB | 8Γ H100 80GB |
| Image Gen (SD) | RTX 3060 12GB | RTX 4090 24GB | A100 cluster |
| Video Gen | A100 40GB | 4Γ A100 | 8β16Γ H100 |
| 3D/NeRF | RTX 3080 10GB | RTX 4090 | A100 40GB |
| AR/VR Inference | Mobile GPU | Jetson AGX | Edge TPU |
Storage & Networking
- NVMe SSDs for fast data loading (3β7 GB/s)
- InfiniBand HDR (200 Gb/s) for multi-node training
- Object storage: S3, GCS, Azure Blob
- RAM requirements: 2β4Γ model size for comfortable training
PHASE 1 β CORE ML & DEEP LEARNING (Months 3β6)
1.1 Classical Machine Learning (Essential Base)
Algorithms
- Linear Regression, Ridge, Lasso, ElasticNet
- Logistic Regression (binary & multiclass)
- Decision Trees: ID3, C4.5, CART
- Ensemble: Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost)
- SVM: kernel trick, RBF, polynomial kernels
- k-NN, k-Means, DBSCAN, Hierarchical Clustering
- Dimensionality reduction: PCA, t-SNE, UMAP
- Bayesian methods: Naive Bayes, Gaussian Processes
Model Evaluation
- Bias-Variance tradeoff
- Cross-validation strategies (k-fold, stratified, time-series)
- Metrics: Accuracy, Precision, Recall, F1, AUC-ROC, mAP, BLEU, FID
- Calibration: Platt scaling, isotonic regression
1.2 Neural Network Fundamentals
Architecture Building Blocks
- Perceptron β MLP (Multi-Layer Perceptron)
- Activation functions: ReLU, LeakyReLU, GELU, SiLU/Swish, Mish, Softmax, Sigmoid
- Loss functions: MSE, MAE, Huber, BCE, CCE, Focal Loss, Contrastive Loss, Triplet Loss
- Regularization: L1/L2, Dropout, DropPath, Label Smoothing
- Batch Normalization, Layer Normalization, Group Normalization, RMS Norm
- Weight initialization: Xavier, He/Kaiming, orthogonal
Backpropagation Deep Dive
- Forward pass: computing activations
- Backward pass: gradient flow via chain rule
- Vanishing/exploding gradient problem & solutions
- Gradient clipping techniques
Convolutional Neural Networks (CNNs)
- Convolution operation: stride, padding, dilation
- Depthwise separable convolutions (MobileNet)
- Pooling: max, average, global average
- Architectures: LeNet β AlexNet β VGG β Inception β ResNet β EfficientNet β ConvNeXt
- Receptive field analysis
- Feature pyramid networks (FPN)
Recurrent Networks
- Vanilla RNN, BPTT (Backprop Through Time)
- LSTM: cell state, forget/input/output gates
- GRU: update/reset gates
- Bidirectional RNNs, Deep RNNs
- Seq2Seq with attention (Bahdanau, Luong)
PHASE 2 β TEXT / NLP / LLM TRACK (Months 4β10)
2.1 Transformer Architecture β Complete Deep Dive
Core Mechanism
- Self-Attention: Q (Query), K (Key), V (Value) matrices
- Attention score: softmax(QKα΅ / βd_k) Γ V
- Multi-Head Attention: h parallel attention heads
- Positional Encoding: sinusoidal (original), learned, RoPE, ALiBi, YaRN
- Feed-Forward Network: two linear layers with GELU/SiLU
- Residual connections + Layer Normalization (Pre-LN vs Post-LN)
- KV Cache: storing key/value pairs for fast autoregressive inference
Attention Variants
- Sparse Attention (Longformer, BigBird)
- Flash Attention v1/v2/v3: IO-aware algorithm, ~3β8Γ speedup
- Multi-Query Attention (MQA), Grouped-Query Attention (GQA)
- Linear Attention, Sliding Window Attention (Mistral)
- Cross-Attention (used in encoder-decoder models)
Architecture Families
Encoder-only (BERT-style)
- BERT, RoBERTa, ALBERT, DeBERTa
- Used for: classification, NER, QA, embeddings
- MLM (Masked Language Modeling) pre-training
Decoder-only (GPT-style)
- GPT series, LLaMA, Mistral, Qwen, Gemma, Phi
- Used for: text generation, instruction following, agents
- Causal language modeling pre-training
Encoder-Decoder (T5-style)
- T5, BART, mT5, FLAN-T5
- Used for: summarization, translation, conditional generation
2.2 Building an LLM from Scratch
Step 1: Tokenization
- Byte-Pair Encoding (BPE): iteratively merging frequent pairs
- WordPiece (BERT), Unigram (SentencePiece)
- Tiktoken (GPT-4 tokenizer)
- Special tokens: [BOS], [EOS], [PAD], [SEP], [MASK]
- Vocabulary size: 32Kβ128K typical
Step 2: Pre-training Data Pipeline
- Data sources: Common Crawl, Wikipedia, Books3, The Pile, RedPajama, DCLM
- Data cleaning: deduplication (MinHash LSH), quality filtering, language detection
- Data mixing ratios (e.g., LLaMA-3: code 17%, web 45%, books 10%...)
- Tokenize β pack into fixed-length sequences β shuffle β stream
Step 3: Model Architecture Design
Input Tokens
β
Token Embedding (vocab_size Γ d_model)
β
Positional Encoding (RoPE)
β
N Γ Transformer Decoder Blocks:
βββ RMSNorm
βββ Multi-Head / GQA Attention + KV Cache
βββ Residual connection
βββ RMSNorm
βββ SwiGLU Feed-Forward Network
βββ Residual connection
β
Final RMSNorm
β
LM Head (d_model Γ vocab_size)
β
Softmax β Next Token Probabilities
Step 4: Training Infrastructure
- Data parallelism (DDP): replicate model, split data
- Tensor parallelism (Megatron-style): split weight matrices
- Pipeline parallelism: split layers across GPUs
- FSDP (Fully Sharded Data Parallel): shard optimizer states
- Gradient checkpointing: trade compute for memory
- ZeRO optimizer (Stage 1/2/3): DeepSpeed
Step 5: Training Procedure
- Warmup steps β cosine LR decay
- Weight decay (AdamW): typically 0.1
- Gradient clipping: max norm 1.0
- BF16 mixed precision training
- Checkpoint every N steps; resume from failure
- Loss monitoring: training perplexity, validation loss
Step 6: Alignment & Fine-Tuning
- SFT (Supervised Fine-Tuning): instruction-response pairs
- RLHF (Reinforcement Learning from Human Feedback):
- Collect human preference data (A vs B comparisons)
- Train reward model
- PPO (Proximal Policy Optimization) fine-tuning
- DPO (Direct Preference Optimization): simpler, no RL needed
- ORPO, GRPO, SimPO: newer preference optimization methods
Step 7: Efficient Fine-Tuning Methods
- LoRA (Low-Rank Adaptation): inject low-rank matrices into attention weights
- QLoRA: quantized base model + LoRA (4-bit NF4 quantization)
- IAΒ³: fewer parameters than LoRA, faster
- Prefix Tuning, Prompt Tuning: soft prompt tokens
- Full fine-tuning with gradient checkpointing
Step 8: Inference Optimization
- Quantization: GPTQ, AWQ, GGUF (llama.cpp), SmoothQuant
- Speculative decoding: small draft model + large verifier
- Continuous batching (vLLM, TGI)
- PagedAttention (vLLM): virtual memory for KV cache
- Beam search, top-k, top-p (nucleus) sampling, temperature
2.3 Serving Text Models as a Service
API Design
- RESTful endpoints: /v1/completions, /v1/chat/completions (OpenAI-compatible)
- Streaming via Server-Sent Events (SSE)
- Rate limiting, auth tokens, usage tracking
Serving Stacks
- vLLM: high-throughput, PagedAttention, OpenAI-compatible API
- TGI (Text Generation Inference by HuggingFace)
- Ollama: local model serving
- LiteLLM: proxy across multiple providers
- Triton Inference Server: NVIDIA, supports TensorRT optimization
RAG System Architecture
User Query
β
Query Embedding (embedding model)
β
Vector Search (FAISS / Chroma / Qdrant / Pinecone / Weaviate)
β
Top-K Relevant Chunks Retrieved
β
Prompt = System + Context Chunks + User Query
β
LLM Generation
β
Response
Key RAG Techniques
- Chunking strategies: fixed-size, semantic, recursive
- Hybrid search: dense (embedding) + sparse (BM25)
- Re-ranking: cross-encoder models (Cohere Rerank, BGE Reranker)
- HyDE (Hypothetical Document Embeddings)
- Parent-child retrieval, sentence window retrieval
PHASE 3 β IMAGE GENERATION & VISION TRACK (Months 6β12)
3.1 Computer Vision Foundations
Core Tasks & Algorithms
- Image Classification: CNN β ViT (Vision Transformer)
- Object Detection: YOLO (v1βv10), SSD, Faster R-CNN, DETR, RT-DETR
- Semantic Segmentation: FCN, DeepLab, SegFormer
- Instance Segmentation: Mask R-CNN, SOLO, SAM (Segment Anything)
- Depth Estimation: MiDaS, DPT, Depth Anything
- Optical Flow: RAFT, FlowNet
- Pose Estimation: OpenPose, MediaPipe, ViTPose
- Image Matching: SIFT, SuperGlue, LoFTR
Vision Transformers (ViT)
- Patch embedding: split image into 16Γ16 patches β linear projection
- Class token [CLS], positional embedding
- Self-attention over patches
- DeiT, BEiT, MAE (Masked Autoencoder), DINO, DINOv2
3.2 Generative Models β Deep Dive
Variational Autoencoders (VAE)
- Encoder β ΞΌ, Ο (latent distribution parameters)
- Reparameterization trick: z = ΞΌ + Ο Γ Ξ΅
- ELBO loss = reconstruction loss + KL divergence
- VQ-VAE: discrete latent space with codebook
- Applications: image compression, latent space for diffusion
Generative Adversarial Networks (GANs)
- Generator G: noise z β fake image
- Discriminator D: real/fake classification
- Min-max game: min_G max_D [log D(x) + log(1-D(G(z)))]
- Training instabilities: mode collapse, vanishing gradients
Key GAN Variants:
- DCGAN: convolutional GAN
- WGAN / WGAN-GP: Wasserstein distance + gradient penalty
- StyleGAN / StyleGAN2 / StyleGAN3: style-based generator, ADA augmentation
- BigGAN: class-conditional, large scale
- Pix2Pix: paired image-to-image translation
- CycleGAN: unpaired image translation
- SPADE / GauGAN: semantic image synthesis
Normalizing Flows
- Invertible transformations: f: x β z, exact likelihood
- RealNVP, Glow, Flow++
- Applications: density estimation, exact log-likelihood
Diffusion Models β Complete Architecture
Forward Process (adding noise):
q(xβ | xβββ) = N(xβ; β(1-Ξ²β)xβββ, Ξ²βI)
x_T β N(0, I) [pure noise after T steps]
Reverse Process (denoising):
p_ΞΈ(xβββ | xβ) = N(xβββ; ΞΌ_ΞΈ(xβ, t), Ξ£_ΞΈ(xβ, t))
Training objective (noise prediction):
L = E[||Ξ΅ - Ξ΅_ΞΈ(βαΎ±β xβ + β(1-αΎ±β)Ξ΅, t)||Β²]
U-Net Denoiser Architecture:
Noisy Image xβ + Timestep t + Text Condition c
β
Encoder blocks (Conv + ResNet + Attention)
β
Bottleneck (Self-Attention + Cross-Attention)
β
Decoder blocks with skip connections
β
Predicted noise Ξ΅_ΞΈ
Latent Diffusion Models (LDM / Stable Diffusion):
- Step 1: Train VAE encoder/decoder (perceptual + adversarial loss)
- Step 2: Encode image to latent: z = E(x), shape [B, 4, H/8, W/8]
- Step 3: Train U-Net/DiT to denoise in latent space
- Step 4: Decode: xΜ = D(zβ)
- Benefit: 8Γ spatial compression β 64Γ cheaper diffusion
Diffusion Samplers:
- DDPM: 1000 steps, slow
- DDIM: 50 steps, deterministic
- DPM-Solver++ : 20 steps
- PNDM, EULER, Heun: various trade-offs
- Consistency Models / LCM: 4β8 steps
Conditioning Mechanisms:
- Text: CLIP, T5, or custom text encoder β cross-attention in U-Net
- Class label: AdaGN (Adaptive Group Normalization)
- Image: ControlNet β copy U-Net encoder + zero-conv layers
- IP-Adapter: image prompt adapter with decoupled cross-attention
Diffusion Architectures
U-Net based (SD1.5, SDXL, Kandinsky):
- ResNet blocks + Self/Cross attention
- Efficient for resolution-aligned generation
DiT β Diffusion Transformer (SD3, FLUX, Sora architecture):
- Treat image patches as tokens
- Standard transformer with adaLN-Zero conditioning
- Scales better with parameters than U-Net
- FLUX.1: hybrid architecture (MM-DiT)
3.3 Text-to-Image: Building Your Own Pipeline
Data Requirements
- LAION-5B, LAION-Aesthetics, Conceptual Captions, JourneyDB
- CLIP filtering for quality/relevance
- Aesthetic scoring (LAION aesthetic predictor)
- Caption generation: LLaMA + BLIP2 for recaptioning
Training Pipeline
Image β VAE Encode β Latent z
Text β Text Encoder β Embeddings c
β
Add noise to z β zβ
β
U-Net/DiT predicts noise: Ξ΅_ΞΈ(zβ, t, c)
β
Loss = MSE(Ξ΅, Ξ΅_ΞΈ) + optional v-prediction
β
Backprop β update U-Net weights
Fine-tuning Methods
- DreamBooth: few-shot personalization, rare token binding
- Textual Inversion: learn new text embeddings only
- LoRA for Diffusion: fine-tune with low-rank adaptation
- HyperDreambooth: faster DreamBooth
- IP-Adapter: plug-and-play image conditioning
Evaluation Metrics
- FID (FrΓ©chet Inception Distance): realism & diversity
- IS (Inception Score): quality & variety
- CLIP Score: text-image alignment
- DINO Score: structural similarity
- Human evaluation (preference studies)
3.4 Image Services Architecture
Client Request (text prompt / image)
β
API Gateway (rate limit, auth, queueing)
β
Job Queue (Redis / RabbitMQ / Celery)
β
Worker Pool (GPU instances)
βββ Load model from cache
βββ CLIP encode prompt
βββ Run diffusion sampling (20β50 steps)
βββ VAE decode
βββ Safety checker / NSFW filter
β
CDN Upload (S3 + CloudFront)
β
Return URL to client
PHASE 4 β VIDEO GENERATION TRACK (Months 10β18)
4.1 Video Understanding Foundations
Video Representations
- Optical flow: per-pixel motion vectors (RAFT, PWCNet)
- Temporal difference frames
- 3D convolutions: (C, T, H, W) tensors
- Video Transformers: TimeSformer, VideoMAE, InternVideo
Key Video Tasks
- Action recognition: TSN, SlowFast, Video Swin
- Video object detection: FCOS + temporal consistency
- Video segmentation: XMem, DEVA
- Dense video captioning: Vid2Seq
- Video question answering: Video-LLaVA
4.2 Video Generation β Architecture Deep Dive
Problem Formulation
Video = sequence of T frames at FPS, each frame (H Γ W Γ 3) Key challenge: temporal consistency + motion coherence + long-range dependencies
Approach 1: Extend Image Diffusion to Video
Temporal Attention Addition:
- Insert temporal self-attention layers between spatial attention layers
- Spatial attn: attend across HΓW pixels in single frame
- Temporal attn: attend across T frames at same spatial position
[B, T, H, W, C]
β
Reshape to [BΓT, HΓW, C] β Spatial Attention
β
Reshape to [BΓHΓW, T, C] β Temporal Attention
β
Reshape back to [B, T, H, W, C]
Models using this approach: ModelScope, Zeroscope, AnimateDiff
Approach 2: 3D U-Net / 3D DiT
3D Convolutions + 3D Attention:
- Replace 2D conv with 3D conv: kernel (kT, kH, kW)
- Pseudo-3D: separate spatial conv + temporal conv in sequence
- Full 3D attention: all TΓHΓW tokens attend to each other (expensive)
Models: Make-A-Video, Imagen Video, VideoCrafter
Approach 3: Full Video DiT (Sora-like)
Video Patch Embedding:
- Divide video into spacetime patches: (p_t, p_h, p_w)
- Flatten patches β sequence of tokens
- Standard transformer applied to all tokens
Video [T, H, W, 3]
β
3D Patch Embed β [N_patches, D] tokens
β
Add spacetime positional encoding (3D RoPE)
β
DiT blocks (self-attn + cross-attn for text)
β
Unpatch β Predicted noise [T, H, W, 3]
Key Models in this category:
- Sora (OpenAI): video DiT at scale
- CogVideoX: open-source video DiT
- Open-Sora / Open-Sora-Plan: community replications
- HunyuanVideo (Tencent): state-of-the-art open model
- Wan2.1: high-quality Chinese open model
- FLUX Video: upcoming
Approach 4: Autoregressive Video Generation
- Tokenize frames with VQVAE β discrete tokens
- Predict next frame tokens with LLM-style transformer
- Models: MAGVIT, VideoGPT, Phenaki
4.3 Video Consistency Techniques
Motion Module (AnimateDiff)
- Plug-in temporal attention module
- Trained on video data, frozen image diffusion weights
- Motion LoRA for specific motion styles
Optical Flow Warping
- Generate keyframe β warp intermediate frames with optical flow
- FILM: frame interpolation for video smoothing
ControlNet for Video
- Per-frame depth/pose/edge control
- Temporal smoothing of control signals
Techniques for Long Video
- Sliding window generation with overlap
- Anchor frame conditioning
- StreamingT2V, FreeNoise
4.4 Video Training Infrastructure
Dataset
- WebVid-10M, Panda-70M, OpenVid-1M, HD-VILA-100M
- Video quality filtering: CLIP score, motion score, aesthetics
- Scene cut detection, deduplication
Training Challenges & Solutions
- Memory: T frames = TΓ memory of image β gradient checkpointing
- Spatial-temporal attention: O(TΒ²HΒ²WΒ²) β sparse attention, window attention
- Multi-resolution training: variable frame sizes and durations
- Progressive training: image β short video β long video
Compute Requirements
- Minimum viable: 8Γ A100 80GB
- Production quality: 64β256Γ H100
- Training time: weeks to months
4.5 Video Services Architecture
User Input (text / image / video)
β
Video Job Scheduler (priority queue)
β
GPU Cluster (multi-node)
βββ VAE Video Encoder (if video input)
βββ Text/Image Encoding
βββ Denoising Loop (T steps Γ N frames)
βββ VAE Video Decoder
β
Post-processing:
βββ Video super-resolution (Real-ESRGAN, RealVSR)
βββ Frame interpolation (RIFE, FILM)
βββ Audio sync (optional: audio generation)
β
Transcode (H.264/H.265/AV1)
β
CDN delivery
PHASE 5 β 3D GENERATION TRACK (Months 12β20)
5.1 3D Representation Methods
Explicit Representations
- Mesh: vertices + faces (triangle mesh), textured with UV maps
- Point Cloud: sparse set of (x,y,z,r,g,b) points
- Voxel Grid: 3D grid of occupied/unoccupied cells
- Signed Distance Function (SDF): f(x,y,z) β distance to nearest surface
Implicit Representations
- NeRF (Neural Radiance Fields): MLP maps (x,y,z,ΞΈ,Ο) β (RGB, Ο)
- Volume rendering: integrate along rays
- Original NeRF, mip-NeRF, NeRF-W, Block-NeRF
- Neural SDF: DeepSDF, NeuS, VolSDF
- Occupancy Networks: binary occupancy prediction
Hybrid Representations
- 3D Gaussian Splatting (3DGS):
- Scene = millions of 3D Gaussians (position, rotation, scale, opacity, color SH)
- Rasterize Gaussians β image (real-time rendering, 100+ FPS)
- 4D Gaussian Splatting for dynamic scenes
- TensoRF: tensor decomposition of radiance fields
- Instant-NGP: hash encoding + small MLP (fast training)
5.2 3D Generation Architectures
Text-to-3D Pipeline β Score Distillation Sampling (SDS)
Concept: Use 2D diffusion model as a "critic" to optimize 3D representation
Initialize 3D (NeRF/Gaussians)
β
Render from random camera viewpoint β image
β
Encode image + Add noise at random t
β
Diffusion model predicts gradient direction
β
Backprop gradient into 3D representation
β
Repeat until 3D matches text description
Key Papers: DreamFusion (SDS), Magic3D (coarseβfine), Fantasia3D, ProlificDreamer (VSD)
Native 3D Generative Models
- Point-E (OpenAI):
- Text β point cloud (diffusion on 3D points)
- Point cloud β mesh via post-processing
- Shap-E (OpenAI):
- Encode 3D assets into latent codes
- Diffusion model in latent space
- Decode to NeRF or mesh
- One-2-3-45: Single image β 3D (multi-view synthesis first)
- Zero123 / Zero123++: Single image β novel view synthesis; Used as backbone for 3D reconstruction
- Large Reconstruction Model (LRM): Image β Triplane NeRF in single forward pass; Transformer architecture, trained on Objaverse; InstantMesh, LGM, CRM variants
- 3D DiT Models (Emerging): Shap-E style but with DiT backbone; Point cloud diffusion with transformer; CraftsMan, Direct3D, Trellis
- Multi-View Diffusion: Generate consistent multi-view images first; Then reconstruct 3D from multi-views; MVDiffusion, SyncDreamer, MVDream, Era3D
5.3 3D Reconstruction Pipeline
Input: Single Image β 3D
Image
β
Feature Extraction (DINOv2/ViT)
β
Triplane Generation (Transformer)
β
Triplane NeRF Rendering
β
Multi-view supervision
β
Mesh Extraction (Marching Cubes / FlexiCubes)
β
Texture Baking
Input: Multi-Image / Video β 3D
Images/Video Frames
β
Camera Pose Estimation (COLMAP / DUSt3R / MASt3R)
β
3D Gaussian Splatting / NeRF fitting
β
Mesh Extraction + Texturing
β
PBR Material Estimation (albedo, roughness, metallic)
3D Asset Generation Workflow
- Text β 3D Mesh + Texture: Shap-E, One-2-3-45++, Meshy AI
- Image β 3D: Zero123++, Trellis, CRM
- Video β 3D: CAT3D, ReconFusion
- 3D Editing: Instruct-NeRF2NeRF, GaussianEditor
5.4 3D Dataset & Training
Datasets
- Objaverse (800K 3D objects), Objaverse-XL (10M+)
- ShapeNet (55 categories, 51K objects)
- ABO (Amazon Berkeley Objects): 147K objects with materials
- GSO (Google Scanned Objects): 1000 real-world objects
- OmniObject3D: diverse real scanned objects
Training Notes
- Render multi-view images from 3D assets
- Use random camera sampling (azimuth, elevation, radius)
- Background augmentation (random color/image)
- DINO features as geometry prior
PHASE 6 β AR/VR/XR INTEGRATION TRACK (Months 16β24)
6.1 Spatial Computing Foundations
Coordinate Systems & Math
- World, Camera, Object, NDC (Normalized Device Coordinates)
- Homogeneous coordinates, projection matrices
- Quaternions for rotation (gimbal lock-free)
- Spatial transformations: Translation, Rotation, Scale (TRS matrices)
- Ray casting & ray marching algorithms
Rendering Pipelines
- Forward Rendering: rasterization pipeline
- Deferred Rendering: G-buffer β lighting pass
- Ray Tracing: physics-accurate lighting
- Gaussian Splatting Rendering: real-time radiance field rendering
- Foveated Rendering: high-res at gaze point, low-res periphery
6.2 AR/VR Hardware Platforms
VR Headsets
- Meta Quest 3: Snapdragon XR2 Gen 2, 8GB RAM, color passthrough
- Apple Vision Pro: M2 + R1 coprocessor, visionOS, eye/hand tracking
- PlayStation VR2: OLED HDR, eye tracking, haptics
- Valve Index / HTC Vive Pro 2: PC-tethered, SteamVR
- Varjo XR-4: photorealistic mixed reality, enterprise
AR Hardware
- Microsoft HoloLens 2: holographic waveguide, Azure Spatial Anchors
- Magic Leap 2: enterprise AR, 70Β° FoV, dimmer control
- Snapchat Spectacles 5: consumer AR glasses
- Ray-Ban Meta: AI-embedded smart glasses
- Orion (Meta): holographic AR glasses prototype
Mobile AR
- ARKit (iOS): LiDAR, scene understanding, face tracking
- ARCore (Android): plane detection, depth API, anchors
- WebXR: AR/VR in browser (no app required)
6.3 Development Platforms & Tools
Game Engines
- Unity 3D:
- AR: AR Foundation (wraps ARKit/ARCore)
- VR: XR Interaction Toolkit
- AI: Sentis (run neural nets in Unity), Muse AI
- Shader: URP/HDRP + ShaderGraph
- Unreal Engine 5:
- OpenXR plugin, MetaXR SDK
- Nanite (virtualized geometry), Lumen (global illumination)
- AI: Neural networks via ONNX Runtime
- Pixel Streaming: stream UE5 experience to browser
- Godot 4: open-source, OpenXR support, Python-like GDScript
Web-Based XR
- Three.js: WebGL 3D + WebXR
- Babylon.js: enterprise WebXR framework
- A-Frame: HTML-based WebVR/AR
- React Three Fiber: React + Three.js
- Model Viewer: Google's <model-viewer> web component
- 8th Wall: WebAR without app install
Spatial AI Frameworks
- SLAM (Simultaneous Localization and Mapping):
- ORB-SLAM3, LSD-SLAM, ElasticFusion
- Deep SLAM: DeepVO, CodeSLAM, iMAP, NICE-SLAM
- Depth estimation: MiDaS, Depth Anything V2, Metric3D
- Hand tracking: MediaPipe Hands, UltraLeap
- Gaze tracking: Tobii, integrated in Apple Vision Pro
- Body tracking: OpenPose, MoveNet, MediaPipe Pose
- Scene understanding: PlaneNet, PanopticFusion, ConceptFusion
6.4 AI-Powered AR/VR Features
Real-Time AI on Device
Neural Rendering:
- Gaussian Splatting viewer (WebGL, Metal, CUDA)
- NeRF real-time inference (Instant-NGP + mobile opt.)
- Neural texture compression
Object Recognition & Segmentation:
- SAM (Segment Anything) for real-time object masking
- YOLO-World for open-vocabulary detection
- Point cloud segmentation: PointNet++, Mask3D
Scene Reconstruction & Completion:
- ScanNet++: high-quality indoor scene dataset
- OpenMask3D: open-vocabulary 3D instance segmentation
- Gaussian Grouping: edit individual objects in Gaussian scenes
AI Avatars:
- Codec Avatars (Meta): photorealistic neural avatars
- Neural Head Avatars: NeRF-based head reconstruction
- SMPL / SMPL-X: parametric body model
- Motion retargeting: motion capture β avatar
Spatial Language Understanding:
- CLIP + 3D: map text to 3D objects (LERF)
- 3D-LLM: LLM with 3D scene understanding
- SpatialBot: spatial reasoning for robots/AR
6.5 AR/VR Service Architecture
Physical World / 3D Assets / AI Models
β
Spatial Understanding Layer:
βββ SLAM (pose tracking)
βββ Plane/mesh detection
βββ Depth estimation
βββ Object recognition
β
AI Processing Layer (on-device + cloud):
βββ 3D object generation (text/image β 3D β place in AR)
βββ Avatar animation
βββ Spatial audio AI
βββ Gesture/gaze recognition
β
Rendering Engine:
βββ Gaussian Splatting / NeRF
βββ PBR mesh rendering
βββ Holographic compositing
βββ Foveated rendering
β
Display Hardware (headset/phone/glass)
PHASE 7 β MULTIMODAL UNIFIED SYSTEMS (Months 18β24+)
7.1 Unified Multimodal Architecture
Any-to-Any Models
- Flamingo / OpenFlamingo: vision-language model
- LLaVA: visual instruction tuning (CLIP + LLaMA)
- CogVLM / InternVL2: strong open VLMs
- Gemini 1.5 / Claude 3.5: native multimodal
- GPT-4o / Gemini: text + image + audio + video
Architecture Pattern:
Text β Text Tokenizer ββββββββββββββββββ
Image β ViT Encoder β Linear Proj ββββββ€
Video β Video Encoder β Temporal Pool ββ€β Unified LLM Backbone β Output
Audio β Whisper / Audio Spec β Proj ββββ€
3D β PointCloud Encoder β Proj βββββββββ
CLIP & Contrastive Learning
- CLIP: image + text encoder trained with contrastive loss
- Align representations so similar concepts are close in embedding space
- SigLIP: sigmoid loss (better than softmax for large batches)
- MetaCLIP, OpenCLIP: open reproductions
7.2 Building an AI Service Platform
Platform Architecture (Production)
βββββββββββββββββββββββββββββββββββββββββββ
β CLIENT LAYER β
β Web App Β· Mobile Β· SDK Β· API β
ββββββββββββββββββββ¬βββββββββββββββββββββββ
β HTTPS / WebSocket
ββββββββββββββββββββΌβββββββββββββββββββββββ
β API GATEWAY LAYER β
β Kong / Nginx / AWS API Gateway β
β Auth (JWT) Β· Rate Limit Β· Routing β
ββββββββββββ¬βββββββββββββββ¬ββββββββββββββββ
β β
ββββββββββββΌβββ ββββββββΌβββββββββββββββ
β TEXT SVC β β MEDIA SERVICES β
β vLLM/TGI β β Image Β· Video Β· 3D β
ββββββββββββ¬βββ ββββββββ¬βββββββββββββββ
β β
ββββββββββββΌβββββββββββββββΌββββββββββββββββ
β GPU COMPUTE CLUSTER β
β Kubernetes + NVIDIA GPU Operator β
β KEDA autoscaling on queue depth β
ββββββββββββ¬βββββββββββββββ¬ββββββββββββββββ
β β
ββββββββββββΌβββ ββββββββΌβββββββββββββββ
β JOB QUEUE β β MODEL REGISTRY β
β Redis/SQS β β MLflow / S3 β
βββββββββββββββ βββββββββββββββββββββββ
PHASE 8 β ALGORITHMS & TECHNIQUES MASTER LIST
8.1 Core Training Algorithms
| Algorithm | Used For | Key Papers |
|---|---|---|
| AdamW | Most model training | Loshchilov 2017 |
| LAMB | Large-batch training | You et al. 2019 |
| Muon | LLM pretraining | Kosson 2024 |
| Lion | Memory-efficient | Chen et al. 2023 |
| SFT | Instruction tuning | - |
| PPO | RLHF | Schulman 2017 |
| DPO | Preference learning | Rafailov 2023 |
| GRPO | Group preference | DeepSeek 2024 |
8.2 Architecture Innovations
| Innovation | Impact | Example |
|---|---|---|
| Flash Attention | 3β8Γ speedup | All LLMs |
| RoPE | Better length generalization | LLaMA, Mistral |
| GQA / MQA | Reduced KV cache | LLaMA3, Gemma |
| SwiGLU | Better than ReLU FFN | PaLM, LLaMA |
| RMSNorm | Faster than LayerNorm | LLaMA series |
| MoE | Scale without compute | Mixtral, Gemini |
| DiT | Scalable diffusion | SD3, FLUX, Sora |
| 3DGS | Real-time 3D | Kerbl 2023 |
8.3 Efficiency Techniques
| Technique | Benefit | Tools |
|---|---|---|
| LoRA/QLoRA | Fine-tune 100Γ cheaper | PEFT library |
| GPTQ | 4-bit weight quantization | AutoGPTQ |
| AWQ | Activation-aware quant | llm-awq |
| Speculative Decoding | 2β3Γ faster inference | vLLM |
| Continuous Batching | Higher GPU utilization | vLLM, TGI |
| INT8/FP8 | 2Γ memory reduction | bitsandbytes |
| KV Cache Compression | Longer context | H2O, ScissorHands |
| Gradient Checkpointing | 4β10Γ memory saving | PyTorch |
PHASE 9 β BUILD IDEAS: BEGINNER β ADVANCED
π’ Beginner Projects (Month 1β6)
- Sentiment Classifier β Fine-tune BERT on movie reviews (IMDb)
- Image Classifier β Train ResNet on CIFAR-10 from scratch
- Simple Chatbot β LLaMA.cpp local + system prompt engineering
- Image Captioner β BLIP-2 inference + Gradio UI
- Style Transfer β Neural style transfer with VGG features
- Object Detector β YOLOv8 fine-tuned on custom dataset
- Text Summarizer β Hugging Face T5/BART pipeline
- RAG Q&A Bot β LangChain + Chroma + LLaMA3
π‘ Intermediate Projects (Month 6β14)
- Custom Image Generator β DreamBooth fine-tuning on personal photos
- Voice-to-Text-to-Image β Whisper + Stable Diffusion pipeline
- Video Dubbing Tool β STT + translate + TTS + lip sync
- 3D Object Creator β Text β Shap-E β GLB download
- AR Product Viewer β Three.js + model-viewer + 3D generation
- Personal LLM Service β vLLM serving + OpenAI-compatible API
- Code Review Bot β LLM fine-tuned on GitHub code review data
- Document Intelligence β OCR + layout parsing + LLM Q&A (DocVQA)
π΄ Advanced Projects (Month 14β24)
- Multimodal Chatbot β LLaVA with image understanding + RAG
- Real-time Video Stylization β ControlNet + optical flow for live video
- 3D Avatar Creator β Face image β SMPL mesh β rigged avatar β AR
- Text-to-World β Text β 3D Gaussian scene β walkable VR environment
- AI-Powered XR Guide β AR app: point camera β AI describes + annotates scene
- Custom Video Generator β Fine-tuned AnimateDiff with motion LoRA
- Spatial Memory System β LLM with 3D scene graph for embodied AI
- Full AI Studio Platform β Unified API for text/image/video/3D with billing
PHASE 10 β REVERSE ENGINEERING METHOD
How to Reverse-Engineer Any Model
Step 1: Use the Model Externally
- Understand inputs/outputs, latency, pricing
- Test edge cases, capabilities, failure modes
- Compare with similar models
Step 2: Find the Architecture
- Read the associated paper (arxiv.org)
- Look for open-source implementations (GitHub, HuggingFace)
- Inspect model checkpoint architecture (model.config.json)
Step 3: Load and Inspect Weights
import torch
model = torch.load('model.pt', map_location='cpu')
for name, param in model.named_parameters():
print(f"{name}: {param.shape}")
- Infer architecture from weight shapes
- Count parameters: sum(p.numel() for p in model.parameters())
Step 4: Trace the Forward Pass
from torch.fx import symbolic_trace
traced = symbolic_trace(model)
print(traced.graph)
Step 5: Reproduce Training
- Find dataset (paper mentions, data cards)
- Replicate preprocessing pipeline
- Start with 1/10 scale, verify loss curves match paper
- Scale up progressively
Step 6: Optimize & Improve
- Apply Flash Attention if missing
- Quantize for faster inference
- Add LoRA fine-tuning support
- Benchmark against original
PHASE 11 β CUTTING-EDGE DEVELOPMENTS (2024β2025)
11.1 LLM Frontiers
- Long Context: Gemini 1.5 (1M tokens), Claude 3.5 (200K), Llama 3.3 (128K)
- Reasoning Models: OpenAI o3, DeepSeek-R1, QwQ (chain-of-thought at inference)
- Mixture of Experts (MoE): Mixtral 8Γ7B, DeepSeek-V3 (671B, 37B active)
- State Space Models: Mamba, Mamba-2, RWKV (linear time complexity)
- Test-Time Compute Scaling: more inference compute β better answers
- Small but Capable: Phi-4, Gemma 3, Qwen3 β 7B models matching older 70B
11.2 Image Generation Frontiers
- FLUX.1: hybrid MM-DiT, state-of-the-art text-to-image
- Stable Diffusion 3.5: improved text rendering, composition
- Real-Time Generation: SDXL-Turbo, FLUX-Schnell, LCM (4 steps)
- Native High Resolution: DiT models scaling beyond 2048Γ2048
- Consistent Characters: IP-Adapter, InstantID, PhotoMaker
- 3D-Aware Generation: Zero123++, Wonder3D, SyncDreamer
11.3 Video Generation Frontiers
- Sora (OpenAI): video as spacetime patches, variable resolution/duration
- HunyuanVideo: open-source, 5-sec HD quality
- Wan2.1: 14B parameter video model, multilingual
- Kling 1.6 / Hailuo: commercial leaders in China
- Video-to-Video: consistent style transfer across full video
- 4D Generation: 3D + motion over time (Animate3D, Consistent4D)
11.4 3D/Spatial AI Frontiers
- 3D Gaussian Splatting (3DGS): real-time radiance field, replacing NeRF
- 4D Gaussian Splatting: dynamic scene reconstruction
- Trellis (Microsoft): unified 3D generation in structured latent space
- DUSt3R / MASt3R: camera-pose-free 3D reconstruction from images
- Splatt3R: instant Gaussian splatting from image pairs
- LiDAR + Vision fusion: SECOND, CenterPoint, BEVFusion for autonomous driving
11.5 AR/VR/XR Frontiers
- Apple Vision Pro: establishes spatial computing paradigm
- Meta Quest 3 / Ray-Ban AI: consumer mixed reality mainstream
- Neural Rendering in XR: Gaussian splatting in Quest 3 (MetaSplat)
- World Models: GAIA-1, DreamerV3 β AI imagines environments
- Holographic Displays: light-field displays, diffractive waveguides
- AI NPCs: LLM-powered real-time characters (Inworld AI, Convai)
- Spatial Foundation Models: models that reason natively in 3D space
11.6 Architecture Frontiers
- Diffusion Transformers (DiT): replacing U-Net across all modalities
- Flow Matching: cleaner training objective than DDPM (Stable Diffusion 3)
- Consistency Models: distill diffusion into 1-step generators
- World Models: predict future from actions (V-JEPA, GAIA, Pandora)
- Multi-modal tokens: unify all modalities in single token vocabulary (Chameleon)
PHASE 12 β RESOURCES, TOOLS & COMMUNITIES
Essential Tools & Libraries
Core ML
- PyTorch, HuggingFace Transformers, Diffusers, PEFT, TRL
- Accelerate (multi-GPU training), bitsandbytes (quantization)
- Flash-Attention-2, xformers
Data & Training
- datasets (HuggingFace), WebDataset, LMDB
- DeepSpeed, Megatron-LM, ColossalAI (distributed training)
- Weights & Biases, MLflow (experiment tracking)
- DVC, LakeFS (data versioning)
Serving & Deployment
- vLLM, TGI, Ollama, LiteLLM
- Triton Inference Server, TensorRT
- ONNX Runtime, OpenVINO (CPU optimization)
- BentoML, Ray Serve, Modal
3D & Spatial
- Open3D, trimesh, PyMeshLab (mesh processing)
- nerfstudio (NeRF + Gaussian framework)
- gsplat (3DGS training library)
- Polyscope (3D visualization)
- COLMAP, hloc (3D reconstruction)
AR/VR Development
- Unity 3D + AR Foundation + XR Interaction Toolkit
- Unreal Engine 5 + OpenXR
- Three.js, Babylon.js (WebXR)
- 8th Wall (WebAR)
- Niantic Lightship (AR platform)
Key Research Venues
- arXiv.org: cs.AI, cs.CV, cs.LG, cs.GR sections
- NeurIPS, ICML, ICLR (ML fundamentals)
- CVPR, ICCV, ECCV (computer vision)
- SIGGRAPH, SIGGRAPH Asia (graphics & rendering)
- ACM MM (multimedia)
Online Learning Resources
- fast.ai (practical deep learning, free)
- Andrej Karpathy's Neural Networks: Zero to Hero (YouTube)
- Stanford CS231n (CNNs for Visual Recognition)
- Stanford CS224N (NLP with Deep Learning)
- Lilian Weng's blog (lilianweng.github.io)
- The Annotated Transformer (Harvard NLP)
- HuggingFace course (free, hands-on)
- Nerfstudio docs (3D/NeRF/Gaussian)
Datasets Hub
- HuggingFace Datasets: largest collection
- Papers With Code: datasets linked to papers
- Roboflow Universe: computer vision datasets
- Objaverse: 3D assets
- Common Voice: multilingual speech
SUMMARY: MASTER TIMELINE
Month 1β3: Foundations (Math, Python, ML basics, Hardware understanding)
Month 3β6: Core DL (CNN, RNN, Transformer theory, hands-on training)
Month 4β10: TEXT TRACK (Build LLM from scratch, fine-tuning, serving)
Month 6β12: IMAGE TRACK (Diffusion models, text-to-image, services)
Month 10β18: VIDEO TRACK (Video diffusion, temporal consistency, pipeline)
Month 12β20: 3D TRACK (NeRF, Gaussian Splatting, text/image-to-3D)
Month 16β24: AR/VR/XR TRACK (Spatial computing, neural rendering, XR apps)
Month 18β24+: UNIFIED PLATFORM (Multimodal, production AI service platform)
Roadmap compiled from: Attention is All You Need (Vaswani 2017), DDPM (Ho 2020), LDM (Rombach 2022), NeRF (Mildenhall 2020), 3DGS (Kerbl 2023), DreamFusion (Poole 2022), Sora (Brooks 2024), DPO (Rafailov 2023), Flash Attention (Dao 2022), LLaMA (Touvron 2023), open research on arXiv, HuggingFace docs, and community best practices.