TildAlice – Tech Blog for Developers
Python, AI, Deep Learning, Data Analysis, Computer Vision, Reinforcement Learning โ tips and tutorials
Latest Posts
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SAC: The Best Algorithm for Continuous Control
Implement Soft Actor-Critic from scratch โ maximum entropy RL, twin Q-networks, automatic temperature tuning, and when to choose SAC over PPO.
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PPO: Why It Powers ChatGPT and Game AI
Deep dive into Proximal Policy Optimization โ the clipping trick, GAE, hyperparameters, and why PPO became the backbone of RLHF in modern LLMs.
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Policy Gradient and Actor-Critic Explained
Master policy gradient methods from REINFORCE to Actor-Critic with GAE. Learn why direct policy optimization unlocks continuous action spaces.
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From Q-Learning to DQN: Your First RL Algorithms
Implement Q-Learning from scratch, then scale up to Deep Q-Networks with experience replay and target networks. Complete PyTorch code included.
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RL Fundamentals: MDP, Bellman Equation, and Value Functions
Learn the foundations of Reinforcement Learning โ MDP framework, policies, value functions, Bellman equation, and exploration strategies with hands-on Python code.
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MCP Tutorial: Build an AI Tool Server in Python
Learn how to build MCP (Model Context Protocol) servers in Python. Step-by-step tutorial with real code examples for tools, resources, and prompts. The open standard backed by Anthropic, OpenAI, and Google.
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GRPO vs PPO for LLM Reasoning: DeepSeek-R1 Explained
How DeepSeek-R1 trains chain-of-thought reasoning with GRPO instead of PPO. Group Relative Policy Optimization removes the critic network, cuts memory by 40%, and produces emergent self-verification behaviors. Full algorithm breakdown with PyTorch code.
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MLOps Cheat Sheet: 12 Production Patterns You’ll Use
12 MLOps patterns from production systems: feature caching, traffic shadowing, schema validation. Real code, not theory.