数学、计算机科学和人工智能汇编
Show HN: Maths, CS and AI Compendium

原始链接: https://github.com/HenryNdubuaku/maths-cs-ai-compendium

本大纲涵盖了现代人工智能和计算的基础广泛主题。它从**数学基础**(向量空间、微积分、统计学、概率论)开始,逐步深入到**核心机器学习**技术,包括深度学习和强化学习。 后续章节深入探讨具体的AI应用:**计算语言学**(NLP、语言模型)、**计算机视觉**(图像/视频处理)和**音频与语音**处理。还包括像**多模态学习**和**自主系统**等新兴领域。 课程内容超越AI,涵盖了必要的**计算原理**(数据结构、算法、操作系统、计算机体系结构)和**硬件加速**(SIMD、GPU编程)。最后,它涉及诸如**推理优化**等高级主题,以及量子机器学习等**交叉领域**,并专门设置部分用于分享正在进行的研究成果。 多个章节标有“Coming”,表示课程将来的扩展。

亨利·恩杜布阿库在 GitHub 上分享了一份全面的、以“直觉优先”为原则的汇编,涵盖数学、计算机科学和人工智能。这份资源历时七年创建,基于他在人工智能/机器学习方面的经验,旨在解决传统教科书的常见问题——符号过于密集、缺乏直觉以及快速过时。 这些笔记已被朋友们成功用于准备 DeepMind 和 OpenAI 等领先人工智能公司的面试,他们全部都获得了工作机会。 这并非旨在作为典型的考试准备工具,而是一份供从业者深入理解底层概念的资源。 恩杜布阿库鼓励有抱负的学生和希望在人工智能研究领域取得进展或攻读博士学位的专业人士探索这份汇编并提供反馈。 它旨在为那些真正*理解*该领域的人们从“基础”开始建立坚实的基础。
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原文
01 Vectors Spaces, magnitude, direction, norms, metrics, dot/cross/outer products, basis, duality Available 02 Matrices Properties, special types, operations, linear transformations, decompositions (LU, QR, SVD) Available 03 Calculus Derivatives, integrals, multivariate calculus, Taylor approximation, optimisation and gradient descent Available 04 Statistics Descriptive measures, sampling, central limit theorem, hypothesis testing, confidence intervals Available 05 Probability Counting, conditional probability, distributions, Bayesian methods, information theory Available 06 Machine Learning Classical ML, gradient methods, deep learning, reinforcement learning, distributed training Available 07 Computational Linguistics syntax, semantics, pragmatics, NLP, language models, RNNs, CNNs, attention, transformers, text diffusion, text OCR, MoE Coming 08 Computer Vision image processing, object detection, segmentation, video processing, SLAM, CNNs, vision transformers, diffusion, flow matching, VR/AR Coming 09 Audio & Speech DSP, ASR, TTS, voice & acoustic activity detection, diarization, source separation, active noise cancelation, wavenet, conformer Coming 10 Multimodal Learning fusion strategies, contrastive learning, VLMs, image tokenizer, video audio co-generation Coming 11 Autonomous Systems perception, robot learning, VLAs, self-driving cars, space robots Coming 12 Computing & OS discreet maths, computer architecture, operating systems, RAM, concurrency, parallelism, programming languages Coming 13 Data Structures & Algorithms arrays, trees, graph, search, sorting, hashmaps Coming 14 SIMD & GPU Programming ARM & NEON, X86 chips, RISC ships, GPUs, TPUs, triton, CUDA, Vulkan Coming 15 Inference quantisation, streamingLLMs, continuous batching, edge inference, Coming 16 Intersecting Fields quantum ML, neuromorphic ML, AI for finace, AI for bio Coming 17 Henry's Research Findings I run many hyperfocused experiemts and will share findings Coming
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