A public account focused on deep learning, computer vision, and autonomous driving perception algorithms, covering visual CV, neural networks, pattern recognition, related hardware and software configurations, and open-source projects.
The article explains that hallucinations in large language models stem mainly from the supervised fine‑tuning stage, illustrates the issue with concrete examples, and presents mitigation techniques such as knowledge‑probing data generation and web‑search tool integration using special tokens.
Tencent’s HunyuanVideo-Avatar converts a static portrait and an audio clip into a lip‑synced, expressive video using a multimodal diffusion Transformer, offering open‑source weights, detailed module designs, hardware requirements, code examples, and a candid assessment of its strengths and current limitations.
This tutorial explains how to construct state feature vectors for reinforcement‑learning value‑function approximation, covering linear, polynomial, Fourier, and radial‑basis representations, as well as state aggregation techniques such as coarse coding and tile coding, and discusses non‑parametric approaches like kernel methods.
This article explains why tabular reinforcement‑learning methods scale poorly, introduces supervised‑learning‑based value‑function approximation using a parameterized vector w, discusses loss design, stochastic‑gradient updates, bootstrapping, semi‑gradient techniques, and linear function approximation, and summarizes practical implications.
Claude 4 introduces two upgraded models—Opus 4, touted as the world’s best coding model, and Sonnet 4 with stronger reasoning—along with new tool‑use capabilities, benchmark wins, a controversial safety test showing opportunistic extortion, and detailed pricing and availability in the Cursor IDE.
This tutorial explains how n-step temporal‑difference (TD) algorithms generalize the one‑step TD and Monte‑Carlo methods, presents the n‑step return update rule, walks through a three‑step TD example, shows how Sarsa and Q‑learning can be extended, and discusses how to choose the optimal n value for a given problem.
This tutorial explains temporal‑difference (TD) learning, compares it with dynamic programming and Monte‑Carlo methods, walks through concrete soccer‑match examples, shows one‑step TD versus constant‑α Monte‑Carlo updates, discusses convergence, bias, and introduces popular TD variants such as Sarsa, Q‑learning, Expected Sarsa and double learning.
This tutorial explains how Monte Carlo methods are enhanced in reinforcement learning through epsilon‑greedy and epsilon‑soft policies, Monte Carlo control, a Blackjack Q‑function example, the distinction between on‑policy and off‑policy learning, importance sampling, and efficient incremental update techniques.
This article explains Monte Carlo methods for reinforcement learning, compares model‑free and model‑based approaches, details V‑ and Q‑function estimation using a Blackjack example, and discusses exploration‑exploitation trade‑offs and practical advantages of MC algorithms.
This article explains how to solve Bellman equations, use iterative policy‑evaluation methods, apply the policy‑improvement theorem, and combine both steps in policy iteration, value iteration, and asynchronous variants, illustrated with a 5‑state example and a 4×4 gridworld.
