February 24, 2025
Updated July 15, 2025
11 minute read
An Introduction to DeepSeek
In the rapidly expanding universe of artificial intelligence, a new name has been making significant waves: DeepSeek. A private Chinese AI company founded in July 2023, DeepSeek has quickly positioned itself as a major contender in the development of large language models (LLMs). Backed by the Chinese hedge fund High-Flyer, the company has distinguished itself through a commitment to creating powerful, open-weight models that challenge the performance of established industry leaders, often at a fraction of the training cost. This approach has not only captured the attention of developers and researchers but has also sent ripples through the global tech market.
Working in the field of AI, and specifically with models like those developed by DeepSeek, offers a front-row seat to one of the most transformative technologies of our time. It's a domain characterized by relentless innovation, where today's breakthrough becomes tomorrow's foundation. For those fascinated by complex problem-solving, the challenge of teaching a machine to reason, write code, or understand nuanced language is a powerful draw. Furthermore, the opportunity to build applications that can reshape industries—from healthcare and finance to creative arts and software development—provides a profound sense of impact and purpose.
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Find a path to becoming a DeepSeek. Learn more at:
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Reading list
We've selected 33 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
DeepSeek.
Provides a practical, hands-on approach to understanding and implementing Transformer models, which are the core architecture behind most large language models, including DeepSeek. Co-authored by Hugging Face engineers, it's highly relevant for anyone looking to work with or understand the technical underpinnings of modern LLMs. The revised edition valuable and up-to-date resource.
Offers a unique perspective by guiding the reader through the process of building an LLM from the ground up. This hands-on approach provides a deep understanding of the internal workings of these models, which is highly relevant to comprehending how DeepSeek is built and trained.
Offers a comprehensive exploration of Transformer architectures specifically for machine learning tasks. It delves into various Transformer models and their applications beyond just NLP, providing a deeper technical understanding of this crucial architecture.
A widely recognized and comprehensive textbook covering the fundamentals of Natural Language Processing (NLP). Understanding NLP is crucial for comprehending how models like DeepSeek process and generate human language. provides a strong theoretical foundation and is often used in both undergraduate and graduate NLP courses.
This recent book delves into the practical aspects of building AI agents using LLMs. Given the course context mentioning AI Agents, this book is highly relevant for understanding how models like DeepSeek can be used to create autonomous and intelligent systems.
Another recent publication focusing on the design and implementation of AI agents, including multi-agent systems. provides a more in-depth look at the architectural considerations and practical challenges of building agent-based applications powered by LLMs.
Focuses specifically on Transformer architectures and their applications in NLP. It covers various Transformer models and provides practical guidance on using them. It's a good resource for understanding the specific architecture that makes LLMs powerful.
Co-authored by a co-founder of DeepMind and Inflection AI, this book explores the transformative impact of AI and other frontier technologies on society. It discusses the challenges of controlling powerful technologies like advanced AI, offering a contemporary perspective on the broader implications of developments like DeepSeek.
Focuses on building applications using LLMs and frameworks like LangChain. It's relevant for understanding how models like DeepSeek can be integrated into practical applications and the ecosystem surrounding them.
Provides a solid understanding of the core concepts and techniques in deep learning. It covers various architectures and applications, offering a good foundation for comprehending the technologies behind advanced AI models like DeepSeek. The second edition recent update.
Bridges the gap between theory and practice, focusing on building real-world NLP systems. It's valuable for understanding how the concepts behind models like DeepSeek are applied in industry and provides practical insights for developing NLP applications.
This widely respected textbook provides a comprehensive introduction to pattern recognition and machine learning from a Bayesian perspective. It covers fundamental concepts that are essential for a deep understanding of the algorithms used in AI models like DeepSeek.
A recent practical guide aimed at making AI agents accessible to a wider audience. It focuses on building and understanding agent-based systems without requiring extensive prior AI knowledge, making it suitable for those interested in the practical applications of models like DeepSeek in creating agents.
This concise book offers a quick and accessible overview of language models, including the concepts behind Transformers and their historical context. It's a good starting point for those who want to quickly grasp the core ideas of LLMs without getting bogged down in excessive detail.
A fundamental text in the field of statistical learning, providing a rigorous treatment of key concepts and techniques used in machine learning. While mathematically intensive, it offers a deep understanding of the statistical foundations relevant to AI model development and analysis.
Dives into the details of Transformer models and their application in NLP. It covers building models from scratch, providing a solid technical understanding of the architecture that is fundamental to large language models like DeepSeek. Suitable for those with a strong technical background wanting to master Transformers.
A widely used introductory book to NLP using the NLTK library in Python. It covers fundamental NLP concepts and techniques with a practical approach. While some topics might be more basic than the advanced models like DeepSeek, it provides a necessary foundation in text processing.
A classic and foundational text in statistical NLP. While older, the principles covered are still relevant for understanding the mathematical and statistical underpinnings of language modeling. It's more theoretical than practical but provides crucial background knowledge.
Addresses the critical intersection of AI with security, privacy, and ethics. Given the increasing deployment of powerful AI models, understanding these aspects is vital for responsible development and use. It provides a comprehensive overview of the challenges and considerations in this space.
Written by a leading AI researcher, this book explores the long-term future of AI and the challenge of ensuring that increasingly intelligent systems remain beneficial to humanity. It's a thought-provoking read on the potential risks and challenges associated with advanced AI.
Offers a critical perspective on the broader societal impact of AI, examining its political and environmental costs. While not technical, it provides essential context for understanding the larger implications of powerful AI models and the industry surrounding them.
Examines the technical challenges and approaches to building algorithms that are fair, accountable, and transparent. It's relevant for understanding the ethical considerations in developing and deploying AI systems, including LLMs, and provides insights into designing responsible AI.
Focuses on applying machine learning using the scikit-learn library in Python. While not covering deep learning in detail, it provides a strong foundation in fundamental ML concepts and practical skills that are useful prerequisites for understanding and working with more complex models like LLMs.
Focuses on Generative Adversarial Networks (GANs), a type of generative model relevant to creating realistic data. While LLMs primarily use transformer architectures, understanding GANs provides broader context in generative AI and practical guide for implementation.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/74o2ol/deepsee
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