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Analyzing Neural Time Series Data
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be u...
Linear Algebra: Theory, Intuition, Code
Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and so on.The way linear algebra is presented in traditional textbooks is different from how professionals use linear algebra in computers to solve real-world applications in machine learning, data science, statistics, and signal processing. For example, the "determinant" of a matrix is important for linear algebra theory, but should you actually use the determinant in practical applications? The answer may surprise you!If you are interested in l...
Practical Linear Algebra for Data Science
If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge...
American Maelstrom
In American Maelstrom, Michael A. Cohen captures the full drama of this watershed election, establishing 1968 as the hinge between the decline of political liberalism and the ascendancy of conservative populism and the anti-government attitudes that continue to dominate the nation's political discourse, taking us to the source of the politics of division.
Medication Errors
In this expanded 600+ page edition, Dr. Cohen brings together some 30 experts from pharmacy, medicine, nursing, and risk management to provide the most current thinking about the causes of medication errors and strategies to prevent them.
What You Accept is What You Teach
While everyone is accountable for their own behavior, leaders are ultimately responsible for employee performance. By setting their own standards of exceptional performance, managers teach their employees to accept accountability for their own actions and attitudes. What You Accept is What You Teach is the perfect "how to" guide for navigating the maze of challenging employee communication and performance problems. It is an excellent resource for developing a healthy culture of accountability and improved employee performance. In use by more than 25,000 managers nation-wide.
The Twenty-something American Dream
In a highly readable blend of analysis and interviews, Cohen shows how today's "twenty-something" generation thinks, examines what their concerns are for themselves and for society, and debunks much of the media hype that portrays this group negatively.
Linear Algebra and Learning from Data
Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Brain-Computer Interfacing
The idea of interfacing minds with machines has long captured the human imagination. Recent advances in neuroscience and engineering are making this a reality, opening the door to restoration and augmentation of human physical and mental capabilities. Medical applications such as cochlear implants for the deaf and neurally controlled prosthetic limbs for the paralyzed are becoming almost commonplace. Brain-computer interfaces (BCIs) are also increasingly being used in security, lie detection, alertness monitoring, telepresence, gaming, education, art, and human augmentation. This introduction to the field is designed as a textbook for upper-level undergraduate and first-year graduate courses in neural engineering or brain-computer interfacing for students from a wide range of disciplines. It can also be used for self-study and as a reference by neuroscientists, computer scientists, engineers, and medical practitioners. Key features include questions and exercises in each chapter and a supporting website.
