Neural networks are networks of artificial neurons or nodes used to solve AI problems. They model connections between nodes as weights, which can be positive or negative depending on the type of connection. Inputs are modified by weights and summed, then an activation function controls the output. Neural networks can be trained via a dataset and can self-learn from experience.

Stanford University

Winter 2023

This course provides a deeper understanding of robot autonomy principles, focusing on learning new skills and physical interaction with the environment and humans. It requires familiarity with programming, ROS, and basic robot autonomy techniques.

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+ 13 more conceptsStanford 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.

No concepts data

+ 35 more conceptsStanford University

Winter 2023

This course is centered on extracting information from unstructured data in language and social networks using machine learning tools. It covers techniques like sentiment analysis, chatbot development, and social network analysis.

No concepts data

+ 14 more conceptsStanford University

Winter 2023

CS 224N provides an in-depth introduction to neural networks for NLP, focusing on end-to-end neural models. The course covers topics such as word vectors, recurrent neural networks, and transformer models, among others.

No concepts data

+ 21 more conceptsStanford University

Winter 2023

This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.

No concepts data

+ 32 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.

No concepts data

+ 13 more conceptsPrinceton University

Spring 2019

This introductory course focuses on machine learning, probabilistic reasoning, and decision-making in uncertain environments. A blend of theory and practice, the course aims to answer how systems can learn from experience and manage real-world uncertainties.

No concepts data

+ 21 more conceptsUniversity of Washington

Winter 2022

A general introduction to computer vision, this course covers traditional image processing techniques and newer, machine-learning based approaches. It discusses topics like filtering, edge detection, stereo, flow, and neural network architectures.

No concepts data

+ 24 more conceptsStanford University

Autumn 2022-2023

Stanford's CS 221 course teaches foundational principles and practical implementation of AI systems. It covers machine learning, game playing, constraint satisfaction, graphical models, and logic. A rigorous course requiring solid foundational skills in programming, math, and probability.

No concepts data

+ 88 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 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 conceptsCarnegie Mellon University

Spring 2018

A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.

No concepts data

+ 55 more conceptsPrinceton University

Fall 2017

A thorough introduction to machine learning principles such as online learning, decision making, gradient-based learning, and empirical risk minimization. It also explores regression, classification, dimensionality reduction, ensemble methods, neural networks, and deep learning. The course material is self-contained and based on freely available resources.

No concepts data

+ 14 more conceptsCarnegie Mellon University

Spring 2022

This course gives an expansive introduction to computer vision, focusing on image processing, recognition, geometry-based and physics-based vision, and video analysis. Students will gain practical experience solving real-life vision problems. It requires a good understanding of linear algebra, calculus, and programming.

No concepts data

+ 19 more concepts