Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer – Stanford University https://stanford.io/3eJW8yT Andrew Ng Adjunct Professor, Computer Science Kian Katanforoosh Lecturer, Computer Science To follow along with the course schedule and syllabus, visit: http://cs230.stanford.edu/
ACHLR ‘The Ethics of Artificial Intelligence: Moral Machines’ Public Lecture Learn more: https://www.qut.edu.au/law/research
Lecture 10 introduces translation, machine translation, and neural machine translation. Google’s new NMT is highlighted followed by sequence models with attention as well as sequence model decoders. ——————————————————————————- Natural Language Processing with Deep Learning Instructors: – Chris Manning – Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition. Lecture 4. Get in touch on Twitter @cs231n, or on Reddit /r/cs231n.
In Lecture 11 we move beyond image classification, and show how convolutional networks can be applied to other core computer vision tasks. We show how fully convolutional networks equipped with downsampling and upsampling layers can be used for semantic segmentation, and how multitask losses can be used for localization and pose estimation. We discuss a number of methods for object detection, including the region-based R-CNN family of methods and single-shot methods like SSD and YOLO. Finally we show how ideas from semantic segmentation and object detection can be combined to perform instance segmentation. Keywords: Semantic segmentation, fully convolutional networks, unpooling, transpose convolution, localization, multitask losses, pose estimation, object detection, sliding window, region proposals, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, DenseCap, instance segmentation, Mask R-CNN Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding [More]
Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/2rf9OO3 Professor Christopher Potts & Consulting Assistant Professor Bill MacCartney, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Potts Professor of Linguistics and, by courtesy, Computer Science Director, Stanford Center for the Study of Language and Information http://web.stanford.edu/~cgpotts/ Consulting Assistant Professor Bill MacCartney Senior Engineering Manager, Apple https://nlp.stanford.edu/~wcmac/ To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/ To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu
Is it reasonable to expect that AI capabilities will eventually exceed those of humans across a range of real-world-decision making scenarios? Should this be a cause for concern, as Elon Musk, Stephen Hawking, and others have suggested? While some in the mainstream AI community dismiss the issue, Professor Russell will argue instead that a fundamental reorientation of the field is required. Instead of building systems that optimise arbitrary objectives, we need to learn how to build systems that will, in fact, be beneficial for us. In this talk, he will show that it is useful to imbue systems with explicit uncertainty concerning the true objectives of the humans they are designed to help. This uncertainty causes machine and human behaviour to be inextricably (and game-theoretically) linked, while opening up many new avenues for research. The ideas in this talk are described in more detail in his new book, “Human Compatible: AI and the Problem of Control” (Viking/Penguin, 2019). About the speaker: Stuart Russell received his BA with first-class honours in physics from Oxford University in 1982 and his PhD in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is Professor (and formerly Chair) of Electrical Engineering and Computer Sciences, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. He has served as an Adjunct Professor of Neurological Surgery at UC San Francisco and as Vice-Chair of the World Economic Forum’s Council on [More]
In Lecture 5 we move from fully-connected neural networks to convolutional neural networks. We discuss some of the key historical milestones in the development of convolutional networks, including the perceptron, the neocognitron, LeNet, and AlexNet. We introduce convolution, pooling, and fully-connected layers which form the basis for modern convolutional networks. Keywords: Convolutional neural networks, perceptron, neocognitron, LeNet, AlexNet, convolution, pooling, fully-connected layers Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture5.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
“Deep Learning to Solve Challenging Problems” For the past seven years, the Google Brain team (g.co/brain) has conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to many Google products. Our group has open-sourced the TensorFlow system (tensorflow.org), a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. We have also collaborated closely with Google’s platforms team to design and deploy new computational hardware called Tensor Processing Units, specialized for accelerating machine learning computations. In this talk, I’ll highlight some of our research accomplishments, and will relate them to the National Academy of Engineering’s Grand Engineering Challenges for the 21st Century, including the use of machine learning for healthcare, robotics, and engineering the tools of scientific discovery. I’ll also cover how machine learning is transforming many aspects of our computing hardware and software systems. Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a [More]
Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/3bhmLce Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn2018.html To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu
In his lecture hosted by BCG GAMMA and Brahe Education Foundation, Max Tegmark speaks about Life 3.0 and the future of Artificial Intelligence, its possibilities and risks. He asks what kind of future do humans want to live in and how can they steer AI towards it? His answers are profound and insightful, but very human-centric for obvious reasons. So lets hear what Sharon Zero has to say about it, from her very unique perspective.
ML Systems Workshop @ NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=8774 Contributed Talk 3: NSML: A Machine Learning Platform That Enables You to Focus on Your Models by Nako Sung. This Video is by Jung-Woo Ha.
Natural Language Processing , Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English. Natural Language Understanding (NLU) Natural Language Generation (NLG)
Artificial intelligence 25 Predicate Logic In ai or predicate logic in artificial intelligence. predicate logic is different from prepositional logic as predicate logic deals with relation and real world entity unlike prepositional logic which is based on true or false predicate logic deals with 3 world entities known as predicate object, predicate function predicate relation. predicate logic has ability to represent facts about object predicate logic enables to represent law and facts from real world entity predicate syntax follow object , relation and function. this video is about predicate logic in artificial intelligence
For all lecture slides you can download form following website http://virtualcomsat.com similarities between artificial intelligence and human intelligence, advantages of human intelligence over artificial intelligence, human intelligence vs artificial intelligence ppt, article about ai vs human, difference between natural intelligence and artificial intelligence in tabular form, is artificial intelligence capturing human intelligence, can artificial intelligence replace human intelligence debate, can artificial intelligence compete with human intelligence, Don’t forget to subscribe my channel Good news for student now you can watch our video by using mobile just install app by using blow link https://play.google.com/store/apps/de… Artificial Intelligence in Urdu|hindi Artificial Intelligence, Philosophy, Psychology, Mathematics, Linguistics, Thinking Humanly, Acting Humanly, Thinking Rationally, Acting Rationally, Biyani Girls College,CS607 Artificial Intelligence artificial intelligence hindi notes,artificial intelligence in hindi notes, https://www.youtube.com/channel/UCgrpR_m474T_CaHrh9tp7WA pleae Subscribe and share
An open conversation with Molly Wright Steenson, hosted by Northeastern University’s Center for Design (CfD).
Professor Christopher Manning, Stanford University & Margaret Mitchell, Google AI http://onlinehub.stanford.edu/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL) To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Lecture 7 continues our discussion of practical issues for training neural networks. We discuss different update rules commonly used to optimize neural networks during training, as well as different strategies for regularizing large neural networks including dropout. We also discuss transfer learning and finetuning. Keywords: Optimization, momentum, Nesterov momentum, AdaGrad, RMSProp, Adam, second-order optimization, L-BFGS, ensembles, regularization, dropout, data augmentation, transfer learning, finetuning Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture7.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
In Lecture 9 we discuss some common architectures for convolutional neural networks. We discuss architectures which performed well in the ImageNet challenges, including AlexNet, VGGNet, GoogLeNet, and ResNet, as well as other interesting models. Keywords: AlexNet, VGGNet, GoogLeNet, ResNet, Network in Network, Wide ResNet, ResNeXT, Stochastic Depth, DenseNet, FractalNet, SqueezeNet Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture9.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
In Lecture 6 we discuss many practical issues for training modern neural networks. We discuss different activation functions, the importance of data preprocessing and weight initialization, and batch normalization; we also cover some strategies for monitoring the learning process and choosing hyperparameters. Keywords: Activation functions, data preprocessing, weight initialization, batch normalization, hyperparameter search Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture6.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
Laws for converting Preposition logic statement into more complex statements for solutions of given problem by using resolution which uses negotiation of a given statement
In Lecture 13 we move beyond supervised learning, and discuss generative modeling as a form of unsupervised learning. We cover the autoregressive PixelRNN and PixelCNN models, traditional and variational autoencoders (VAEs), and generative adversarial networks (GANs). Keywords: Generative models, PixelRNN, PixelCNN, autoencoder, variational autoencoder, VAE, generative adversarial network, GAN Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture13.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/