BreCaspian/Deep_Learning_Foundation_and_Concepts-Springer
🌟The complete open-source release of the book 《 Deep Learning: Foundations and Concepts 》 and its related supporting resources
This resource provides comprehensive materials for learning deep learning, from foundational theories to advanced architectures. It includes a full textbook, code examples with Jupyter notebooks for practical exercises like handwritten digit recognition, and complete mathematical solutions for all textbook problems. It's designed for anyone new to machine learning, experienced practitioners, students, or self-learners interested in understanding deep learning theory.
Use this if you want to gain a solid theoretical and practical understanding of deep learning, from basic concepts to modern architectures like Transformers.
Not ideal if you are looking for a quick reference guide or a tool for immediate, high-level application of existing deep learning models without diving into their underlying principles.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Jan 09, 2026
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