[COURSE] Neural Networks: Zero to Hero (#717)

* Create Neural Networks: Zero to Hero.en.md

Best intro to AI course by former head of AI at OpenAI/Tesla - Andrej Karpathy

* Create Neural Networks: Zero to Hero.md

add Chinese version

this is the best course so far for intro to AI. tought by former head of AI at Open AI, Andrej Karpathy

docs/人工智能/Neural Networks: Zero to Hero.en.md

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+# Neural Networks: Zero to Hero  
+
+## Description  
+
+- **Instructor:** Andrej Karpathy  
+- **Prerequisites:** Basic Python programming and some familiarity with deep learning concepts  
+- **Programming Language:** Python  
+- **Difficulty:** 🌟🌟🌟🌟  
+- **Class Hours:** Approximately 19 hours  
+
+This hands-on deep learning course, taught by Andrej Karpathy, provides a detailed and intuitive introduction to neural networks and their underlying principles. The course starts with foundational concepts such as backpropagation and micrograd before progressing to building language models, WaveNets, and GPT from scratch. The emphasis is on practical implementation, with step-by-step coding explanations to help students understand and build complex models from the ground up.  
+
+## Instructor Information  
+
+Andrej Karpathy is a renowned AI researcher and educator with extensive experience in deep learning and neural networks. He was the **Senior Director of AI at Tesla**, leading the **computer vision team for Tesla Autopilot** from 2017 to 2022. Prior to that, he was a **research scientist and founding member at OpenAI** (2015-2017). In 2023, he returned to OpenAI, contributing to improvements in GPT-4 for ChatGPT. In 2024, he founded **Eureka Labs**, an AI+Education company.  
+
+Karpathy holds a **PhD from Stanford University**, where he worked on convolutional and recurrent neural networks with **Fei-Fei Li**. He has collaborated with leading AI researchers, including **Daphne Koller, Andrew Ng, Sebastian Thrun, and Vladlen Koltun**. He also taught the first deep learning course at Stanford, **CS 231n: Convolutional Neural Networks for Visual Recognition**, which became one of the largest classes at the university.  
+
+## Course Resources  
+
+- **Lecture Videos:** [YouTube Playlist](https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)  
+- **Assignments:** Self-guided projects and code implementation exercises available throughout the lectures  
+
+For more information, watch the full playlist on YouTube.  

docs/人工智能/Neural Networks: Zero to Hero.md

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+# 神经网络：从零到英雄  
+
+## 课程简介  
+
+- **讲师：** Andrej Karpathy  
+- **先修要求：** 具备基本的 Python 编程能力，并对深度学习概念有所了解  
+- **编程语言：** Python  
+- **难度：** 🌟🌟🌟🌟  
+- **课程时长：** 约 19 小时  
+
+本课程由 Andrej Karpathy 讲授，是一个深入浅出的深度学习课程，旨在帮助学习者掌握神经网络的核心原理。课程从基础概念（如反向传播和 micrograd）入手，逐步带领学员构建语言模型、WaveNet，并从零开始实现 GPT。课程以实践为主，提供逐步讲解的代码示例，让学员能够理解并构建复杂的神经网络模型。  
+
+## 讲师信息  
+
+Andrej Karpathy 是一位知名的人工智能研究员和教育者，在深度学习和神经网络领域具有丰富的经验。他曾在 **2017 至 2022 年担任特斯拉 AI 部门高级总监**，领导 **Tesla Autopilot 计算机视觉团队**，负责数据标注、神经网络训练、部署等工作。在此之前，他曾是 **OpenAI 的研究科学家和创始成员**（2015-2017）。2023 年，他回归 OpenAI，参与改进 ChatGPT 的 GPT-4。2024 年，他创立了 **Eureka Labs**，一家专注于 AI + 教育的公司。  
+
+Karpathy 拥有 **斯坦福大学博士学位**，师从 **Fei-Fei Li（李飞飞）**，主要研究卷积神经网络和循环神经网络及其在计算机视觉和自然语言处理中的应用。他曾与 **Daphne Koller、Andrew Ng（吴恩达）、Sebastian Thrun 和 Vladlen Koltun** 等知名研究员合作。此外，他还在斯坦福大学教授了首个深度学习课程 **CS 231n: 卷积神经网络与视觉识别**，该课程逐渐发展为斯坦福大学规模最大的课程之一。  
+
+## 课程资源  
+
+- **课程视频：** [YouTube 播放列表](https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)  
+- **作业：** 课程中提供的代码实践和项目练习  
+
+更多信息请访问 YouTube 观看完整课程视频。