Abadi, Martín, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. 2016. “TensorFlow: A System for Large-Scale Machine Learning,” May. https://arxiv.org/abs/1605.08695.
Agrawal, Akshay, Akshay Naresh Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, et al. 2019. “TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning” ArXiv.org
Ansel, Jason, Edward Yang, Horace He, Natalia Gimelshein, Animesh Jain, Michael Voznesensky, Bin Bao, et al. 2024. “PyTorch 2: Faster Machine Learning through Dynamic Python Bytecode Transformation and Graph Compilation.” ACM, April.
Abelson, Harold. 1996. Structure and Interpretation of Computer Programs, Second Edition. MIT Press.
Aho, Alfred V, Monica S Lam, Ravi Sethi, and Jeffrey D Ullman. 2015. Compilers: Principles, Techniques, & Tools. Pearson.
Aggarwal, Charu C. 2020. Linear Algebra and Optimization for Machine Learning. Springer.
Bastien, Frédéric, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian Goodfellow, Arnaud Bergeron, Nicolas Bouchard, David Warde-Farley, and Yoshua Bengio. 2025. “Theano: New Features and Speed Improvements.” ArXiv.org
Baydin, Atilim Gunes, Barak A Pearlmutter, Radul, Alexey Andreyevich, and Jeffrey Mark Siskind. 2015. “Automatic Differentiation in Machine Learning: A Survey.” ArXiv.org
Bright, Paige, Alan Edelman, and Steven G Johnson. 2025. “Matrix Calculus (for Machine Learning and Beyond).” ArXiv.org
Blondel, Mathieu, and Vincent Roulet. 2024. “The Elements of Differentiable Programming.” ArXiv.org
Chen, Tianqi, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, et al. 2018. “TVM: An Automated End-To-End Optimizing Compiler for Deep Learning.” ArXiv, February.
Cho, Kyunghyun. 2025. “Machine Learning: A Lecture Note.” ArXiv.org
Cooper, Keith D, and Linda Torczon. 2022. Engineering a Compiler. Morgan Kaufmann.
Cormen, Thomas H, Charles Eric Leiserson, Ronald L Rivest, and Clifford Stein. 2009. Introduction to Algorithms. MIT Press.
Frostig, Roy, Google Brain, Matthew Johnson, and Chris Leary Google. n.d. “Compiling Machine Learning Programs via High-Level Tracing.”
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Cambridge, Massachusetts: The MIT Press.
Griewank, Andreas, and Andrea Walther. 2009. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. Philadelphia, Pa.: Society For Industrial & Applied Mathematics ; Cambridge.
Hack, Sebastian. 2007. Register Allocation for Programs in SSA Form.
Harris, Charles R., K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, et al. 2020. “Array Programming with NumPy.” Nature 585 (7825): 357–62.
Harris, Sarah. 2021. Digital Design and Computer Architecture: RISC-V Edition. S.L.: Morgan Kaufmann Publisher.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning, Second Edition : Data Mining, Inference, and Prediction. 2nd ed. New York: Springer.
Hennessy, John L, and David A Patterson. 2025. Computer Architecture: A Quantitative Approach. Cambridge, Ma: Morgan Kaufmann.
Hwu, Wen-Mei W, David B. Kirk, and Izzat El Hajj. 2022. Programming Massively Parallel Processors: A Hands-on Approach. S.L.: Morgan Kaufmann.
Jia, Yangqing, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. “Caffe: Convolutional Architecture for Fast Feature Embedding.” ArXiv.org
Jurafsky, Dan , and James H. Martin. 2025. “Speech and Language Processing.” Stanford.edu. 2025.
Kang, Wanmo, and Kyunghyun Cho. 2025. “Linear Algebra for Data Science.” 2025.
Klein, Philip N. 2013. Coding the Matrix: Linear Algebra through Applications to Computer Science. Newton, Mass: Newtonian Press.
Krishnamurthi, Shriram. 2025. “Programming Languages: Application and Interpretation.” 2025.
Lattner, Chris, Mehdi Amini, Uday Bondhugula, Albert Cohen, Andy Davis, Jacques Pienaar, River Riddle, Tatiana Shpeisman, Nicolas Vasilache, and Oleksandr Zinenko. 2020. “MLIR: A Compiler Infrastructure for the End of Moore’s Law.” ArXiv:2002.11054
Lay, David C, Steven R Lay, and Judith Mcdonald. 2016. Linear Algebra and Its Applications. Boston: Pearson.
Mackay, David J C. 2003. Information Theory, Inference, and Learning Algorithms. Cambridge: Cambridge University Press.
Møller, Anders, and Michael I Schwartzbach. 2024. “Static Program Analysis.” Cs.au.dk. 2024.
Murphy, Kevin P. 2023. Probabilistic Machine Learning: Advanced Concepts. MIT Press.
Murphy, Kevin P. 2022. Probabilistic Machine Learning: An Introduction. Cambridge: MIT Press.
Ng, Andrew, and Tengyu Ma. 2023. CS229 Lecture Notes.
Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. 2019. “PyTorch: An Imperative Style, High-Performance Deep Learning Library.” ArXiv.org
Ragan-Kelley, Jonathan, Connelly Barnes, Andrew Adams, Sylvain Paris, Frédo Durand, and Saman Amarasinghe. 2013. “Halide.” Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation.
Saeta, Brennan, Denys Shabalin, Marc Rasi, Brad Larson, Xihui Wu, Parker Schuh, Michelle Casbon, et al. 2021. “Swift for TensorFlow: A Portable, Flexible Platform for Deep Learning.” ArXiv.or
Scardapane, Simone. 2025. “Alice’s Adventures in a Differentiable Wonderland.” ArXiv.org
Suhan, Alex, Davide Libenzi, Ailing Zhang, Parker Schuh, Brennan Saeta, Jie Young Sohn, and Denys Shabalin. 2021. “LazyTensor: Combining Eager Execution with Domain-Specific Compilers.” ArXiv.org
Spector, Benjamin F, Simran Arora, Aaryan Singhal, Daniel Y Fu, and Christopher Ré. 2024. “ThunderKittens: Simple, Fast, and Adorable AI Kernels.” ArXiv.org
Stepanov, Alexander A, and Daniel E Rose. 2015. From Mathematics to Generic Programming. Upper Saddle River, Nj: Addison-Wesley.
Stepanov, Alexander, and Paul McJones. 2019. Elements of Programming. Semigroup Press.
Tarjan, Robert E. 1988. Data Structures and Network Algorithms. Philadelphia: Society For Industrial And Applied Mathematics.
Tokui, Seiya, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, Shunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, and Hiroyuki Yamazaki Vincent. 2019. “Chainer: A Deep Learning Framework for Accelerating the Research Cycle.” ArXiv.org
Tillet, Philippe, Hsiang-Tsung Kung, and David G Cox. 2019. “Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations.” ACM.
Trefethen, Lloyd N, and David Bau. 1997. Numerical Linear Algebra. SIAM.
Uwe Naumann. 2012. The Art of Differentiating Computer Programs: An Introduction to Algorithmic Differentiation. Philadelphia: Society For Industrial And Applied Mathematics.