Build Your Own AI Engineering Projects
来自 Wikiprompt,自由的提示词百科全书
Build Your Own AI Engineering Projects A comprehensive list of 50 hands-on AI engineering projects to build from scratch, covering models, systems, and tools.
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Build your own Reasoner (Chain of Thought implementation)
Build your own Agent loop (ReAct pattern)
Build your own Inference Server (in C++/Rust)
Build your own Transformer from scratch (Attention is all you need)
Build your own Vector Database (HNSW index)
Build your own RAG pipeline
Build your own Flash Attention kernel (CUDA)
Build your own Quantization library (Int8/FP4 implementation)
Build your own Mixture of Experts (MoE) routing layer
Build your own Distributed training loop (FSDP/Tensor Parallelism)
Build your own KV Cache paging system (like vLLM)
Build your own Speculative Decoding system
Build your own State Space Model (Mamba implementation)
Build your own RLHF pipeline (PPO implementation)
Build your own Small Language Model (SLM)
Build your own Matrix Multiplication kernel
Build your own LoRA (Low-Rank Adaptation) trainer
Build your own Code interpreter sandbox
Build your own DPO (Direct Preference Optimization) loss function
Build your own Graph RAG system
Build your own Model merger (Model Soups/Spherical Linear Interpolation)
Build your own Interpretability tool (SAE - Sparse Autoencoders)
Build your own Synthetic data generator
Build your own Function Calling router
Build your own Structured Output parser (Context Free Grammars)
Build your own Multi-modal projector (CLIP implementation)
Build your own LLM Eval harness
Build your own Guardrails system (Input/Output filtering)
Build your own Prompt caching mechanism
Build your own Tokenizer (BPE implementation)
Build your own Autograd engine (like Micrograd)
Build your own Diffusion model (UNet + Scheduler)
Build your own Vision Transformer (ViT)
Build your own Whisper-style ASR model
Build your own Text-to-Speech pipeline
Build your own Semantic Router
Build your own Knowledge Graph builder
Build your own Data curation pipeline (MinHash/Deduplication)
Build your own AI Gateway (Load balancing/Failover)
Build your own Parameter Efficient Fine-Tuning (PEFT) library
Build your own Text-to-SQL engine
Build your own Recommendation system (Two-tower architecture)
Build your own Embedding model
Build your own Logit Processor
Build your own Softmax kernel optimization
Build your own Adversarial attack generator
Build your own Audio Spectrogram transformer
Build your own Neural Architecture Search
Build your own Model Distillation pipeline
Build your own Feature Store
Build your own Database driver (for Vectors)
用法
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