This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com
From playlist Intro to Data Science
In this video, I present some applications of artificial neural networks and describe how such networks are typically structured. My hope is to create another video (soon) in which I describe how neural networks are actually trained from data.
From playlist Machine Learning
What is Neural Network in Machine Learning | Neural Network Explained | Neural Network | Simplilearn
This video by Simplilearn is based on Neural Networks in Machine Learning. This Neural Network in Machine Learning Tutorial will cover the fundamentals of Neural Networks along with theoretical and practical demonstrations for a better learning experience 🔥Enroll for Free Machine Learning
From playlist Machine Learning Algorithms [2022 Updated]
Star Network - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Probabilistic logic programming and its applications - Luc De Raedt, Leuven
Probabilistic programs combine the power of programming languages with that of probabilistic graphical models. There has been a lot of progress in this paradigm over the past twenty years. This talk will introduce probabilistic logic programming languages, which are based on Sato's distrib
From playlist Logic and learning workshop
Deep Learning with Neural Networks and TensorFlow Introduction
Welcome to a new section in our Machine Learning Tutorial series: Deep Learning with Neural Networks and TensorFlow. The artificial neural network is a biologically-inspired methodology to conduct machine learning, intended to mimic your brain (a biological neural network). The Artificial
From playlist Machine Learning with Python
Neural Network Architectures & Deep Learning
This video describes the variety of neural network architectures available to solve various problems in science ad engineering. Examples include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Book website: http://databookuw.com/ Steve Brunton
From playlist Data Science
Generative Model Basics - Unconventional Neural Networks p.1
Hello and welcome to a series where we will just be playing around with neural networks. The idea here is to poke around with various neural networks, doing unconventional things with them. Doing things like trying to teach a sequence to sequence model math, doing classification with a gen
From playlist Unconventional Neural Networks
Neural Networks 1 Neural Units
From playlist Week 5: Neural Networks
Atılım Güneş Baydin: "Universal Probabilistic Programming in Simulators"
Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Universal Probabilistic Programming in Simulators" Atılım Güneş Baydin, University of Oxford Abstract: We present a novel probabilistic programming framework that coupl
From playlist Machine Learning for Physics and the Physics of Learning 2019
Probability theory and AI | The Royal Society
Join Professor Zoubin Ghahramani to explore the foundations of probabilistic AI and how it relates to deep learning. 🔔Subscribe to our channel for exciting science videos and live events, many hosted by Brian Cox, our Professor for Public Engagement: https://bit.ly/3fQIFXB #Probability #A
From playlist Latest talks and lectures
Josh Tenenbaum - The cognitive science perspective: Reverse-engineering the mind (CCN 2017)
Presented at Cognitive Computational Neuroscience (CCN) 2017 (http://www.ccneuro.org) held September 6-8, 2017.
From playlist AI talks
Probabilistic Graphical Models (PGMs) In Python | Graphical Models Tutorial | Edureka
🔥 Post Graduate Diploma in Artificial Intelligence by E&ICT Academy NIT Warangal: https://www.edureka.co/executive-programs/machine-learning-and-ai This Edureka "Graphical Models" video answers the question "Why do we need Probabilistic Graphical Models?" and how are they compare to Neural
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
What Kind of Computation Is Cognition?
Recent successes in artificial intelligence have been largely driven by neural networks and other sophisticated machine learning tools for pattern recognition and function approximation. But human intelligence is much more than finding patterns or approximating functions. And no machine sy
From playlist Whitney Humanities Center
Lecture 19: Generative Models I
Lecture 19 is the first of two lectures about generative models. We compare supervised and unsupervised learning, and also compare discriminative vs generative models. We discuss autoregressive generative models that explicitly model densities, including PixelRNN and PixelCNN. We discuss a
From playlist Tango
Deep Learning Lecture 10.1 - Probabilistic Models
Introduction to Probabilistic Models
From playlist Deep Learning Lecture
Safety and robustness for deep learning with provable guarantees - Marta Kwiatkowska - Oxford
Computing systems are becoming ever more complex, with automated decisions increasingly often based on deep learning components. A wide variety of applications are being developed, many of them safety-critical, such as self-driving cars and medical diagnosis. Since deep learning is unstabl
From playlist Interpretability, safety, and security in AI
Fellow Short Talks: Professor Zoubin Ghahramani, University of Cambridge
Bio Zoubin Ghahramani FRS is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group, and The Alan Turing Institute’s University Liaison Director for Cambridge. He is also the Deputy Academic Director of the Leverhulme Centre for the
From playlist Short Talks
