#python

Learn AI Series (#119) - Experiment Tracking and Reproducibility What will I learn You will learn why an ML experiment you cannot repeat is not really an experiment at all -- it's an anecdote; MLflow for

Learn AI Series (#118) - Data Engineering for AI What will I learn You will learn the difference between batch and streaming data pipelines, and (more importantly) when each one actually earns its complexity;

Learn AI Series (#117) - ML System Design What will I learn You will learn how to frame a vague business goal as a precise machine-learning problem -- the translation step where most projects quietly go

Learn AI Series (#116) - Mini Project: Training a Game-Playing AI What will I learn You will learn how to wire an entire RL training pipeline together, from raw environment to a trained agent that actually

Learn Python Basics Together 🚀 | Day 5 Welcome back to our beginner-friendly Python learning series! In today's video, we learn: ✅ Modules and Packages in Python ✅ Importing Functions from Another File

Welcome back to our Python learning series! In today's video, we learn about one of the most useful data structures in Python: ✅ Dictionaries in Python This video is beginner friendly and suitable for:

Learn AI Series (#115) - Offline Reinforcement Learning What will I learn You will learn why learning from a fixed, frozen dataset -- with no environment to poke at -- is both wildly useful and genuinely

Learn AI Series (#114) - Inverse Reinforcement Learning What will I learn You will learn the core reversal at the heart of inverse RL -- inferring a reward function from observed behaviour, rather than

Learn AI Series (#113) - RL for Real-World Applications What will I learn You will learn sim-to-real transfer -- training a policy in a physics simulator and then deploying it on real hardware without

Learn AI Series (#112) - RL for Games What will I learn You will learn AlphaZero -- the single algorithm that mastered Chess, Go and Shogi with nothing but the rules and a stubborn habit of playing itself;

Learn AI Series (#111) - Multi-Agent Reinforcement Learning What will I learn You will learn the three flavours of multi-agent RL: cooperative (shared reward), competitive (zero-sum), and mixed (a bit

Learn AI Series (#110) - Model-Based Reinforcement Learning What will I learn You will learn the real difference between model-free and model-based RL -- and why the second family exists at all; how to

Learn AI Series (#109) - Advanced Policy Optimization What will I learn You will learn what a trust region is, and why bounding how far a policy may move in one update is the single idea that turns flaky

团委老师在群里突然又发通知，让大家填一个电子表格，还有扫描《入团志愿书》到一个PDF文件，并把表格和PDF文件发给班级团支书。 (插图由ChatGPT辅助生成) 填表格这事是轻车熟路啦，但如何把《入团志愿书》扫描成一个PDF文件，却让我犯了愁，群里很多同学是去复印社花钱搞定，我可舍不得这笔巨款呢。 而且家里的打印机就自带扫描功能，我就不信解决不了这个问题。

Learn AI Series (#108) - Policy Gradient Methods What will I learn You will learn why directly optimizing the policy is sometimes a far better idea than learning a value function first; the policy gradient

Learn AI Series (#107) - Deep Q-Networks (DQN) What will I learn You will learn why tabular Q-Learning falls apart the moment the state space gets big, and why a lookup table was never going to scale to

Learn AI Series (#106) - Temporal Difference Learning What will I learn You will learn TD(0): updating value estimates from single transitions, without ever waiting for an episode to finish; bootstrapping:

Learn AI Series (#105) - Monte Carlo Methods What will I learn You will learn how to estimate value functions from complete episodes of experience -- no model of the environment required; first-visit vs