complete eng_version for deep learning folder

docs/深度学习/CS224n.en.md

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+# CS224n: Natural Language Processing
+
+## Course Overview
+
+- University：Stanford
+- Prerequisites：Fundations of Deep Learning + Python
+- Programming Language：Python
+- Course Difficulty：🌟🌟🌟🌟
+- Estimated Hours：80 hours
+
+CS224n is an introductory course in Natural Language Processing (NLP) offered by Stanford and led by renowned NLP expert Chris Manning, the creator of the word2vec algorithm. The course covers core concepts in the field of NLP, including word embeddings, RNNs, LSTMs, Seq2Seq models, machine translation, attention mechanisms, Transformers, and more.
+
+The course consists of 5 progressively challenging programming assignments covering word vectors, the word2vec algorithm, dependency parsing, machine translation, and fine-tuning a Transformer.
+
+The final project involves training a Question Answering (QA) model on the well-known SQuAD dataset. Some students' final projects have even led to publications in top conferences.
+
+## Course Resources
+
+- Course Website：<http://web.stanford.edu/class/cs224n/index.html>
+- Course Videos：Searcg for 'CS224n' on Bilibili <https://www.bilibili.com/>
+- Course Textbook：N/A
+- Course Assignments：<http://web.stanford.edu/class/cs224n/index.html>，5 Programming Assignments + 1 Final Project
+
+## Resource Compilation
+
+All resources and assignment implementations used by @PKUFlyingPig during the course are compiled in [PKUFlyingPig/CS224n - GitHub](https://github.com/PKUFlyingPig/CS224n)
+

docs/深度学习/CS285.en.md

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+# CS285: Deep Reinforcement Learning
+
+## Course Overview
+
+- University：UC Berkeley
+- Prerequisites：CS188, CS189
+- Programming Language：Python
+- Course Difficulty：🌟🌟🌟🌟
+- Estimated Hours：80 hours
+
+The CS285 course, currently taught by Professor Sergey Levine, covers various aspects of deep reinforcement learning. It is suitable for students with a foundational understanding of machine learning, including concepts such as Markov Decision Processes (MDPs). The course involves a substantial amount of mathematical formulas, so a reasonable mathematical background is recommended. Additionally, the professor regularly updates the course content and assignments to reflect the latest research developments, making it a dynamic learning experience.
+
+For course content access, as of the Fall 2022 semester, the teaching format involves pre-recorded videos for students to watch before class. The live sessions mainly focus on Q&A, where the professor discusses selected topics from the videos and answers students' questions. Therefore, the provided course video links already include all the content. The assignments consist of five programming projects, each involving the implementation and comparison of classical models. Occasionally, assignments may also include the reproduction of recent models. The final submission typically includes a report. Given that assignments provide a framework and often involve code completion based on hints, the difficulty level is not excessively high.
+
+In summary, this course is suitable for beginners entering the field of deep reinforcement learning. Although the difficulty increases as the course progresses, it offers a rewarding learning experience.
+
+## Course Resources
+
+- Course Website: <http://rail.eecs.berkeley.edu/deeprlcourse/>
+- Course Videos: <https://www.youtube.com/playlist?list=PL_iWQOsE6TfX7MaC6C3HcdOf1g337dlC9>
+- Course Texbook: N/A
+- Course Assignments: <http://rail.eecs.berkeley.edu/deeprlcourse/>, 5 programming assignments

docs/深度学习/LHY.en.md

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+# National Taiwan University: Machine Learning by Hung-yi Lee
+
+## Course Overview
+
+- University：National Taiwan University
+- Prerequisites：Proficiency in Python
+- Programming Language：Python
+- Course Difficulty：🌟🌟🌟🌟
+- Estimated Hours：80 hours
+
+Professor Hung-yi Lee, a professor at National Taiwan University, is known for his humorous and engaging teaching style. He often incorporates fun elements like Pokémon into his slides, making the learning experience enjoyable.
+
+Although labeled as a machine learning course, the breadth of topics covered is impressive. The course includes a total of 15 labs covering Regression, Classification, CNN, Self-Attention, Transformer, GAN, BERT, Anomaly Detection, Explainable AI, Attack, Adaptation, RL, Compression, Life-Long Learning, and Meta Learning. This wide coverage allows students to gain insights into various domains of deep learning, helping them choose areas for further in-depth study.
+
+Don't be overly concerned about the difficulty of the assignments. All assignments come with example code from teaching assistants, guiding students through data processing, model building, and more. Students are required to make modifications based on the provided code. This presents an excellent opportunity to learn from high-quality code, and the assignments serve as valuable resources for those looking to breeze through course projects.
+
+## Course Resources
+
+- Course Websites：<https://speech.ee.ntu.edu.tw/~hylee/ml/2022-spring.php>
+- Course Videos：<https://speech.ee.ntu.edu.tw/~hylee/ml/2022-spring.php>
+- Course Textbook：N/A
+- Course Assignments：<https://speech.ee.ntu.edu.tw/~hylee/ml/2022-spring.php>, 15 labs covering a wide range of deep learning domains