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A GitHub repository providing a complete Chinese Markdown version of 'Computer Systems: A Programmer's Perspective' with exercises, answers, and lab instructions, along with an online reading website.
A tweet recommends CS336 as a starting point for learning about LLMs, architectures, and kernels.
An open-source, free online English grammar project designed to systematically build a solid foundation in English grammar through graded difficulty levels, supporting self-study and classroom use.
A tweet from @real_deep_ml recommending a machine learning book and highlighting the Deep-ML platform's approach to teaching core concepts with NumPy and projects with scikit-learn or PyTorch.
A GitHub repository providing a structured 100-day learning plan for LLM inference engineering, covering topics from CUDA kernels to autoscaling, with runnable scripts.
GitHub_Daily recommends an open-source project called ai-agents-from-scratch. It starts from zero and uses local models to gradually implement core patterns such as tool calling, memory, and ReAct loops, helping developers understand the underlying principles of AI Agents without directly using frameworks.
FreeCodeCamp published a full book teaching how to build a production-ready DevSecOps platform from homelab to AWS, covering Kubernetes, CI/CD, GitOps, security scanning, and observability.
Zig by Example is a hands-on introduction to the Zig programming language, featuring annotated example programs for version 0.16.
The tweet shares a compressed video version of the essence of the Google and Kaggle joint AI Agent 5-day special course, removing fluff and repetitive content, retaining core dry goods.
Recommends an open-source Chinese AI Agent general knowledge tutorial website, including 21 chapters with visualized animated tutorials, a 130+ question bank, and an AI teaching assistant. It is purely static and can be deployed to Nginx with one click.
Recommend 'The Little Book of Reinforcement Learning' as an introductory textbook for reinforcement learning. It's short but covers fundamentals to algorithms, and comes with PyTorch implementations and supplementary derivations, suitable for understanding core RL concepts.
A tweet promoting a GitHub repository containing over 300 real ML system design case studies from major companies like Google, Amazon, Microsoft, and Netflix, aiming to teach how production ML systems are actually built.
Kyle Kingsbury shares a free outline for a 16-32 hour distributed systems fundamentals class, covering theory, algorithms, and practical production concerns, with optional labwork via Maelstrom.
A set of four cards covering the core concepts of neural networks: neuron, forward pass, activations, and backpropagation, aimed at helping learners understand how models from perceptrons to transformers work.
GPU Mode is a learning resource featuring a YouTube series, GitHub repo with slides/notebooks, and a practice website for mastering CUDA programming.
Introducing the codecrafters-io/build-your-own-x repository, a collection of tutorials for building various technologies from scratch, helping developers understand underlying principles through hands-on practice.
Introduces an open-source course called 'AI Engineering from Scratch', containing 503 lessons, covering from linear algebra to autonomous swarms, with special emphasis on Agent Engineering and Multi-Agent phases.
A tweet recommending the 'Hands-on Modern RL' website as the best resource to learn reinforcement learning from scratch, with a link to a chapter on BipedalWalker.
An interactive 3D step-by-step guide to learning how LLMs work, covering key transformer concepts like embedding, self-attention, and softmax. It recommends a visual approach over reading papers.
A comprehensive system design master tree covering fundamentals through real-world applications, including architecture patterns, databases, caching, messaging systems, API design, and deployment strategies. Intended as a structured learning guide for software engineers.