Mathematical optimization is the process of finding the best element from a set of available alternatives, based on some criterion. It is divided into two subfields: discrete and continuous optimization. Optimization problems arise in many quantitative disciplines and have been studied for centuries. The general approach involves maximizing or minimizing a real function by systematically choosing input values from an allowed set.
Stanford University
Fall 2022
An in-depth course focused on building neural networks and leading successful machine learning projects. It covers Convolutional Networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Students are expected to have basic computer science skills, probability theory knowledge, and linear algebra familiarity.
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+ 35 more conceptsUniversity of Washington
Autumn 2019
A survey course on neural network implementation and applications, including image processing, classification, detection, and segmentation. The course also covers semantic understanding, translation, and question-answering applications. It's ideal for those with a background in Machine Learning, Neural Networks, Optimization, and CNNs.
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+ 13 more conceptsUC Berkeley
Fall 2022
UC Berkeley's CS 188 course covers the basic ideas and techniques for designing intelligent computer systems, emphasizing statistical and decision-theoretic modeling. By the course's end, students will have built autonomous agents that can make efficient decisions in a variety of settings.
No concepts data
+ 20 more conceptsUC Berkeley
Fall 2022
Explores how compilers translate high-level languages into machine-understandable code, offering practical experience with developing compilers for various languages. Also covers reasoning about compiler correctness and understanding runtime errors.
No concepts data
+ 28 more conceptsCarnegie Mellon University
Spring 2020
This course provides a comprehensive introduction to deep learning, starting from foundational concepts and moving towards complex topics such as sequence-to-sequence models. Students gain hands-on experience with PyTorch and can fine-tune models through practical assignments. A basic understanding of calculus, linear algebra, and Python programming is required.
No concepts data
+ 40 more conceptsStanford University
Autumn 2022
This Stanford University course delves into the depths of computer systems and programming. It continues from the introductory sequence, expanding students' programming experience using the C language, exploring machine-level code, computer arithmetic, memory management, and more.
No concepts data
+ 25 more conceptsStanford University
Winter 2023
This course examines the intersections of philosophy, public policy, social science, and engineering in the context of recent computing technology and platforms. Key areas of focus include algorithmic decision-making, data privacy, AI, the influence of private computing platforms, and issues of diversity in tech. Students need to have completed CS106A.
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+ 17 more conceptsBrown University
Spring 2022
Brown University's Deep Learning course acquaints students with the transformative capabilities of deep neural networks in computer vision, NLP, and reinforcement learning. Using the TensorFlow framework, topics like CNNs, RNNs, deepfakes, and reinforcement learning are addressed, with an emphasis on ethical applications and potential societal impacts.
No concepts data
+ 40 more concepts