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Quantum Computation and Quantum Information
One of the most cited books in physics of all time, Quantum Computation and Quantum Information remains the best textbook in this exciting field of science. This 10th anniversary …
Introduction to Electrodynamics
In this new edition of the standard undergraduate textbook on electricity and magnetism, David Griffiths provides expanded discussions on topics such as the nature of field lines, …
Wilson and Walker's Principles and Techniques of Biochemistry and Molecular Biology
Bringing this best-selling textbook right up to date, the new edition uniquely integrates the theories and methods that drive the fields of biology, biotechnology and medicine, …
The Macroeconomics of Decarbonisation
Decarbonisation is the reduction of carbon dioxide emissions using low carbon power sources, lowering output of greenhouse gasses into the atmosphere. This is essential to meet …
Mastering Communication with Seriously Ill Patients
Physicians who care for patients with life-threatening illnesses face daunting communication challenges. Patients and family members can react to difficult news with sadness, …
Introduction to Quantum Mechanics
Changes and additions to the new edition of this classic textbook include a new chapter on symmetries, new problems and examples, improved explanations, more numerical problems to …
Spacetime and Geometry
Spacetime and Geometry is an introductory textbook on general relativity, specifically aimed at students. Using a lucid style, Carroll first covers the foundations of the theory …
Descartes: Meditations on First Philosophy
Descartes's Meditations on First Philosophy remains one of the most widely studied works of Western philosophy. This volume is a refreshed and updated edition of John Cottingham's …
Essential Grammar in Use with Answers
Grammar in Use is the world's best-selling grammar series for learners of English. Essential Grammar in Use with Answers, authored by Raymond Murphy, is the first choice for …
Mathematics for Machine Learning
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, …