reading

Books that I am actively studying.

• Seven Concurrency Models in Seven Weeks
  Paul Butcher

• Notas sobre Procesamiento de Lenguaje Natural: De fundamentos clásicos a modelos neuronales y LLMs
  Rubén F. Manrique

Books that I have studied substantially and in depth.
Being in this section does not necessarily mean that I have read every chapter from cover to cover.

• Topics in Algebra and Analysis: Preparing for the Mathematical Olympiad
  Radmila Bulajich Manfrino, José Antonio Gómez Ortega, and Rogelio Valdez Delgado

• Análise Real, Vol. 1
  Elon Lages Lima

• Proofs: A Long-Form Mathematics Textbook
  Jay Cummings

• Numerical Analysis
  Richard L. Burden, J. Douglas Faires, and Annette M. Burden

• Introduction to Algorithms (CLRS)
  Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

• Classic Computer Science Problems in Python
  David Kopec

• Computer Science From Scratch: Building Interpreters, Art, Emulators and ML in Python
  David Kopec

• Competitive Programming 4: The Lower Bound of Programming Contests in the 2020s — Book 1
  Steven Halim, Felix Halim, and Suhendry Effendy

• Fluent Python
  Luciano Ramalho

• Python Workout: 50 Ten-Minute Exercises
  Reuven M. Lerner

• Understanding Deep Learning
  Simon J. D. Prince

Books that I would like to study in the future.

• Topology Without Tears
  Sidney A. Morris

• The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks
  Daniel A. Roberts, Sho Yaida, and Boris Hanin

• Operating Systems: Three Easy Pieces
  Remzi H. Arpaci-Dusseau and Andrea C. Arpaci-Dusseau

• Modern Compiler Implementation in ML
  Andrew W. Appel