Chapter 2: The Efficiency Revolution: PEFT and Its Next Generation
LoRA (Low-Rank Adaptation)Continue reading on Towards AI »
Artificial Intelligence, deep-learning, llm, Machine Learning, Technology
deep-learning
Artificial Intelligence, data-science, deep-learning, llm, neural-networks
The Algorithm That Cheats at Math (And Why That’s Genius)aka HNSW
You Never Find the Closest Vector. And That’s the Whole Point.Continue reading on Towards AI »
attention mechanisms, deep-learning, deepseek-v3, kv cache optimization, large-language-models, mla, multi-head latent attention, pytorch, pytorch tutorial, RoPE, rotary positional embeddings, transformer architecture, transformers, tutorial
Build DeepSeek-V3: Multi-Head Latent Attention (MLA) Architecture
Table of Contents Build DeepSeek-V3: Multi-Head Latent Attention (MLA) Architecture The KV Cache Memory Problem in DeepSeek-V3 Multi-Head Latent Attention (MLA): KV Cache Compression with Low-Rank Projections Query Compression and Rotary Positional Embeddings (RoPE) Integration Attention Computation with Multi-Head Latent…
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ai, deep-learning, llm, Machine Learning
New LLM Architecture Gallery
I put together a new LLM Architecture Gallery that collects the architecture figures from my recent comparison articles in one place, together with compact fact sheets and links.
ai, deep-learning, llm, Machine Learning
A Dream of Spring for Open-Weight LLMs: 10 Architectures from Jan-Feb 2026
A Round Up And Comparison of 10 Open-Weight LLM Releases in Spring 2026
ann, approximate nearest neighbor, cosine similarity, deep-learning, embeddings, faiss, flat index, hnsw, ivf, RAG, recall at k, retrieval augmented generation, semantic-search, tutorial, vector database, Vector Databases, vector-search
Vector Search with FAISS: Approximate Nearest Neighbor (ANN) Explained
Table of Contents Vector Search with FAISS: Approximate Nearest Neighbor (ANN) Explained From Exact to Approximate: Why Indexing Matters The Trouble with Brute-Force Search The Curse of Dimensionality Enter the Approximate Nearest Neighbor (ANN) Accuracy vs. Latency: The Core Trade-Off…
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ai, deep-learning, llm, Machine Learning
State of AI 2026 with Sebastian Raschka, Nathan Lambert, and Lex Fridman
I recently sat down with Lex Fridman and Nathan Lambert for a comprehensive 4.5 h interview to discuss the current state of progress of AI, and what the…
computer-vision, deep-learning, image segmentation, Meta AI, open-vocabulary, PCS, promptable concept segmentation, promptable visual segmentation, Prompting, PVS, sam 3, segment anything, tutorial, vision transformers
SAM 3: Concept-Based Visual Understanding and Segmentation
Table of Contents SAM 3: Concept-Based Visual Understanding and Segmentation The Evolution of Segment Anything: From Geometry to Concepts Core Model Architecture and Technical Components The Perception Encoder (PE) and Vision Backbone The Open-Vocabulary Text and Exemplar Encoders The DETR-Based…
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ai, deep-learning, llm, Machine Learning
Categories of Inference-Time Scaling for Improved LLM Reasoning
Inference scaling has become one of the most effective ways to improve answer quality and accuracy in deployed LLMs. The idea is straightforward. If we are…
ai, deep-learning, llm, Machine Learning
The State Of LLMs 2025: Progress, Problems, and Predictions
A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.