Description This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level
To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will provide a solid introduction to the field of reinforcement learning and students will learn about the
Logistics All course announcements take place through the CS230 Ed forum. Please make sure to join! Class components CS230 has the following components: In-person lecture - once a week at Hewlett Teaching Center 200. You can access lectures by going to the “Panopto Course Videos” tab of Canvas. Video lectures, programming assignments, and quizzes on Coursera A midterm covering material from the fi
eBook How autonomous systems are changing day-to-day life in the U.S. Explore the practical and ethical implications of automation. Webinar How to use AI to enhance user experience Learn how to design better, smarter user interactions by integrating artificial intelligence into your user experience. Webinar Looking ahead toward the future of robotics Learn how we create robots and autonomous syste
講義 京都大学集中講義：機械学習と深層学習の数理と応用 2018年12月17日～20日 レポート課題 (2019年1月10日締め切り．提出先：数学教室事務室) レポート課題１: Pythonによる深層学習の実装（"初学者向け"・"非初学者向け"のどちらかを解いてください） 初学者向け：Google colabを用いて以下のipython notebookに書いてある内容に従い実行し，４つの課題の結果を報告してください． Pythonの基本的な文法.ipynb， scikit-learn を用いた機械学習.ipynb, 深層学習を用いた画像認識.ipynb 非初学者向け：自分で興味のある深層学習モデルを学習し，その結果を報告してください．問題は画像認識でも自然言語処理でもなんでも構いません． レポート課題２：問題を解いて提出してください レポート課題２のpdf． 講義資料 初回：機械学習概要
Deep Learning Course You can find here slides, recordings, and a virtual machine for François Fleuret's deep-learning courses 14x050 of the University of Geneva, Switzerland. This course is a thorough introduction to deep-learning, with examples in the PyTorch framework: machine learning objectives and main challenges, tensor operations, automatic differentiation, gradient descent, deep-learning s
Growing Importance of Deep Learning Deep learning underpins a lot of important and increasingly important applications today ranging from facial recognition, to self-driving cars, to medical diagnostics and more. Made for Anyone Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learnin
Introduction to Deep Learning with flavor of Natural Language Processing (NLP) This site accompanies the latter half of the ART.T458: Advanced Machine Learning course at Tokyo Institute of Technology, which focuses on Deep Learning for Natural Language Processing (NLP). Lecture #1: Feedforward Neural Network (I) Keywords: binary classification, Threshold Logic Units (TLUs), single-layer neural net
Logistics Lectures: are on Tuesday/Thursday 4:30 PM - 5:50 PM Pacific Time in NVIDIA Auditorium. The lectures will also be livestreamed on Canvas via Panopto. Lecture videos for enrolled students: are posted on Canvas (requires login) shortly after each lecture ends. Unfortunately, it is not possible to make these videos viewable by non-enrolled students. Publicly available lecture videos and vers
MIT 6.S191 Introduction to Deep Learning MIT's introductory program on deep learning methods with applications in computer vision, robotics, medicine, language, game play, art, and more! Description An efficient and high-intensity bootcamp designed to teach you the fundamentals of deep learning as quickly as possible! MIT's introductory program on deep learning methods with applications to natural
A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems. This free course is designed for people (and bunnies!) with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. Deep learning can do all kinds of amazing things. For instance, all illustra
CS 598 LAZ: Cutting-Edge Trends in Deep Learning and Recognition Instructor: Svetlana Lazebnik (slazebni -at- illinois.edu) Lectures: T TH 12:30-1:45, 216 Siebel Instructor office hours: Tuesdays 2-3PM or by appointment, 3308 Siebel TA: Arun Mallya (amallya2 -at- illinois.edu) TA office hours: Mondays 2-3PM, Wednesdays 3-4PM, 3340 Siebel Always check announcements on Piazza for short-notice chang
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