Machine Learning Algorithms Implemented with NumPy

Detailed review of a 30k-line NumPy machine learning repository implementing 30+ models with explicit gradient computations, utilities, and test examples.

What Is an RNN (Recurrent Neural Network)?

Technical overview of RNNs and LSTM architectures, how they model sequential data, application areas like signal and text processing, and MATLAB-based implementation.

LLM Inference Optimization for NLP Applications

System-level overview of LLM inference optimization, detailing techniques and tradeoffs to improve throughput for Transformer-based large language models.

Medical Robots in Healthcare Applications

Overview of medical robots in digital health, covering surgical and microrobot applications, AI/ML-enabled perception, sensors, system architecture, and market trends.

Resolution-Refutation Logic Reasoning for Large Language Models

GFaiR applies resolution-refutation over natural language to improve first-order logic reasoning in LLMs, boosting generalization and faithfulness with a validator.

Beginner's Guide to Intelligent Computing Centers

Overview of intelligent computing center architecture and operation: GPU clusters, high-speed storage and networking, distributed frameworks, intelligent OS, and AI access models.

F-Learning: Forget-then-Learn for LLM Knowledge Updates

F-Learning: a parameter-based fine-tuning paradigm that subtracts knowledge parameter deltas to forget outdated facts, then fine-tunes (LoRA or full-model) to update LLM knowledge.

Key Factors of Compute for Large AI Models

Concise technical overview of GPU concepts, architecture and GPU vs CPU differences, parallel processing and performance factors for graphics and AI inference.

Self-Supervised Monocular Depth Estimation in Dynamic Scenes

Review of Dynamo-Depth: a self-supervised monocular depth method that jointly learns depth, 3D independent flow and motion segmentation to handle dynamic scenes.

Vision Transformer Basics and Object Detection

Overview of Vision Transformer architectures and their use in object detection, covering encoder-decoder design, multi-scale fusion, DETR and Deformable DETR approaches.