This page contains a list academic papers and literature pertaining to machine learning research that should be considered required reading for all students starting ML projects.
Resources available online which reference to freely available literature.
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9 Seminal Deep Learning Papers
Seminal works on CNN development with a focus on image classification, segmentation, and object localization. These are the core methods that newer state-of-the-art methods all build off of. Read all of these.
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Alexander Jung's repository of paper summaries
A researcher has been compiling a github repository of summaries of the literature they read. Consistent formatting, accurate and meaningful summaries, sensible curation of the reported results and figures.
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As an example here is a summary of the Wide Residual Networks Paper (2016).
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The intention here is NOT to avoid reading the described paper, but to introduce yourself to the concepts at play to preempt the terseness of the full paper.
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Cited chronology of highly influential papers in different sub-fields with download links to pdf's.
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A review of the field written by leading scientists in the field Yann Lecun, Yoshua Bengio, and Geoffrey Hinton.
Free to download as .pdf files. Incredibly important resources for understanding the theory and the problem domain. You cannot develop a solution to a problem you don't understand.
Foundation in core techniques and fundamental mathematics for machine learning. Start here:
- The Hundred-Page Machine Learning Book - Burkov
- Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares - Boyd and Vandenberghe
More advanced books by leading researchers, offering a deeper look at the theory of machine learning:
Other interesting topics to learn about: