Berkeley ee 140 homework solutions

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The difference is in what else you take: mainly engineering, or mainly humanities and social sciences. It includes the theory of computation, the design and analysis of algorithms, the architecture and logic design of computers, programming languages, compilers, operating systems, scientific computation, computer graphics, databases, artificial intelligence, and natural language processing. Our goal is to prepare students both for a possible research career and long-term technical leadership in industry. We must therefore look beyond today's technology and give students the primary ideas and the learning skills that will prepare them to teach themselves about tomorrow's technology. Information on this GPA and the process to petition for admission to the major can be found on the Petitioning to the Computer Science Major website. Transfer students admitted to Berkeley must apply separately to the Computer Science major after matriculating and completing the missing prerequisite courses for declaration.
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Berkeley EECS

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Electrical Engineering (EL ENG) < University of California, Berkeley

This class provides a practical introduction to deep learning, including theoretical motivations and how to implement it in practice. As part of the course we will cover multilayer perceptrons, backpropagation, automatic differentiation, and stochastic gradient descent. Moreover, we introduce convolutional networks for image processing, starting from the simple LeNet to more recent architectures such as ResNet for highly accurate models. Throughout the course we emphasize efficient implementation, optimization and scalability, e. The goal of the course is to provide both a good understanding and good ability to build modern nonparametric estimators.
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Electrical Engineering (EL ENG)

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Catalog Description: This course and its follow-on course EE16B focus on the fundamentals of designing modern information devices and systems that interface with the real world. Together, this course sequence provides a comprehensive foundation for core EECS topics in signal processing, learning, control, and circuit design while introducing key linear-algebraic concepts motivated by application contexts. The courses are aimed at entering students as well as non-majors seeking a broad foundation for the field. Units: 4.

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