add english version for machine learning systems

docs/机器学习系统/AICS.en.md

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+# Intelligent Computing Systems
+
+## Course Overview
+
+- University: University of Chinese Academy of Sciences
+- Prerequisites: Computer Architecture, Deep Learning
+- Programming Languages: Python, C++, BCL
+- Course Difficulty: 🌟🌟🌟
+- Estimated Hours: 100+ hours
+
+Intelligent computing systems serve as the backbone for global AI, producing billions of devices annually, including smartphones, servers, and wearables. Training professionals for these systems is critical for China's AI industry competitiveness. Understanding intelligent computing systems is vital for computer science students, shaping their core skills.
+
+Prof. Yunji Chen's course, taught in various universities, uses experiments to provide a holistic view of the AI tech stack. Covering deep learning frameworks, coding in low-level languages, and hardware design, the course fosters a systematic approach.
+
+Personally, completing experiments 2-5 enhanced my grasp of deep learning frameworks. The BCL language experiment in chapter five is reminiscent of CUDA for those familiar.
+
+I recommend the textbook for a comprehensive tech stack understanding. Deep learning-savvy students can start from chapter five to delve into deep learning framework internals.
+
+Inspired by the course, I developed a [simple deep learning framework](https://github.com/ysj1173886760/PyToy) and plan a tutorial. Written in Python, it's code-light, suitable for students with some foundation. Future plans include more operators and potential porting to C++ for balanced performance and efficiency.
+
+
+## Course Resources
+
+- Course Website：[Official Website](https://novel.ict.ac.cn/aics/)
+- Course Videos：[bilibili](https://space.bilibili.com/494117284)
+- Course Textbook："Intelligent Computing Systems" by Chen Yunji
+- Course Assignments：6 experiments (including writing a convolutional operator, adding operators to TensorFlow, writing operators in BCL and integrating them into TensorFlow, etc.) (specific content can be found on the official website)
+- Experiment Manual：[Experiment 2.0 Guide Manual](https://forum.cambricon.com/index.php?m=content&c=index&a=show&catid=155&id=708)
+- Study Notes：<https://sanzo.top/categories/AI-Computing-Systems/>, notes summarized based on the experiment manual
+
+## Resource Compilation
+
+All resources and homework implementations used by @ysj1173886760 in this course are consolidated in [ysj1173886760/Learning: ai-system - GitHub](https://github.com/ysj1173886760/Learning/tree/master/ai-system)

docs/机器学习系统/CMU10-414.en.md

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+# CMU 10-414/714: Deep Learning Systems
+
+## Course Overview
+
+- University: Carnegie Mellon University (CMU)
+- Prerequisites: Introduction to Systems (e.g., 15-213), Basics of Deep Learning, 
+                 Fundamental Mathematical Knowledge
+- Programming Languages: Python, C++
+- Difficulty: 🌟🌟🌟
+- Estimated Hours: 100 hours
+
+The rise of deep learning owes much to user-friendly frameworks like PyTorch and TensorFlow. Yet, many users remain unfamiliar with these frameworks' internals. If you're curious or aspiring to delve into deep learning framework development, this course is an excellent starting point.
+
+Covering the full spectrum of deep learning systems, the curriculum spans top-level framework design, autodifferentiation principles, hardware acceleration, and real-world deployment. The hands-on experience includes five assignments, building a deep learning library called Needle. Needle supports automatic differentiation, GPU acceleration, and various neural networks like CNNs, RNNs, LSTMs, and Transformers.
+
+Even for beginners, the course gradually covers simple classification and backpropagation optimization. Detailed Jupyter notebooks accompany complex neural networks, providing insights. For those with foundational knowledge, assignments post autodifferentiation are approachable, offering new understandings.
+
+Instructors [Zico Kolter](https://zicokolter.com/) and [Tianqi Chen](https://tqchen.com/)  released open-source content. Online evaluations and forums are closed, but local testing in framework code remains. Hope for an online version next fall.
+
+## Course Resources
+
+- Course Website：<https://dlsyscourse.org>
+- Course Videos：<https://www.youtube.com/watch?v=qbJqOFMyIwg>
+- Course Assignments：<https://dlsyscourse.org/assignments/>
+
+## Resource Compilation
+
+All resources and assignment implementations used by @PKUFlyingPig in this course are consolidated in [PKUFlyingPig/CMU10-714 - GitHub](https://github.com/PKUFlyingPig/CMU10-714)

docs/机器学习系统/MLC.en.md

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+# Machine Learning Compilation
+
+## Course Overview
+
+- University: Online course
+- Prerequisites: Foundations in Machine Learning/Deep Learning
+- Programming Language: Python
+--Difficulty: 🌟🌟🌟
+- Estimated Hours: 30 hours
+
+This course, offered by top scholar Chen Tianqi during the summer of 2022, focuses on the field of machine learning compilation. As of now, this area remains cutting-edge and rapidly evolving, with no dedicated courses available domestically or internationally. If you're interested in gaining a comprehensive overview of machine learning compilation, this course is worth exploring.
+
+The curriculum predominantly centers around the popular machine learning compilation framework [Apache TVM](https://tvm.apache.org/), co-founded by Chen Tianqi. It delves into transforming various machine learning models developed in frameworks like Tensorflow, Pytorch, and Jax into deployment patterns with higher performance and adaptability across different hardware. The course imparts knowledge at a relatively high level, presenting macro-level concepts. Each session is accompanied by a Jupyter Notebook that provides code-based explanations of the concepts. If you are involved in TVM-related programming and development, this course offers rich and standardized code examples for reference.
+
+All course resources are open-source, with versions available in both Chinese and English. The course recordings can be found on both Bilibili and YouTube in both languages.
+
+## Course Resources
+
+- Course Website：<https://mlc.ai/summer22-zh/>
+- Course Videos：[Bilibili][Bilibili_link]
+- Course Notes：<https://mlc.ai/zh/index.html>
+- Course Assignments：<https://github.com/mlc-ai/notebooks/blob/main/assignment>
+
+[Bilibili_link]: https://www.bilibili.com/video/BV15v4y1g7EU?spm_id_from=333.337.search-card.all.click&vd_source=a4d76d1247665a7e7bec15d15fd12349