Deep learning is a machine learning technique that learns features and tasks directly from data. This data can include images, text, or sound. The video uses an example image recognition problem to illustrate how deep learning algorithms learn to classify input images into appropriate ca
From playlist Introduction to Deep Learning
Deep Learning SIMPLIFIED: The Series Intro - Ep. 1
Are you overwhelmed by overly-technical explanations of Deep Learning? If so, this series will bring you up to speed on this fast-growing field – without any of the math or code. Deep Learning is an important subfield of Artificial Intelligence (AI) that connects various topics like Machi
From playlist Deep Learning SIMPLIFIED
Linear regression as a shallow neural network
It's time for everyone involved or interested in healthcare to learn about deep neural networks. In this video I continue with the example of linear regression to build an intuitive understanding of deep learning. It all comes together in this video and after watching it, we will be one
From playlist Introduction to deep learning for everyone
This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole course: https://go.umd.edu/jbg-inst-808 (Including homeworks and reading.) Music: https://soundcloud.com/alvin-grissom-ii/review-and-rest
From playlist Deep Learning for Information Scientists
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Fac
From playlist Deep Learning SIMPLIFIED
This video follows on from the discussion on linear regression as a shallow learner ( https://www.youtube.com/watch?v=cnnCrijAVlc ) and the video on derivatives in deep learning ( https://www.youtube.com/watch?v=wiiPVB9tkBY ). This is a deeper dive into gradient descent and the use of th
From playlist Introduction to deep learning for everyone
Deep Learning with R for Beginners
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. #Deep_learning architectures such as deep neural ne
From playlist Deep Learning
Basic linear algebra for deep learning
This a series for healthcare professionals and anyone else interested in learning how to create deep neural networks. In this video tutorial I demonstrate the very basic principles of linear algebra. For a more comprehensive view of the topic watch my playlist here: https://www.youtube.c
From playlist Introduction to deep learning for everyone
GRCon20 - Deep learning inference in GNU Radio with ONNX
Presented by Oscar Rodriguez and Alberto Dassatti at GNU Radio Conference 2020 https://gnuradio.org/grcon20 This paper introduces gr-dnn, an open source GNU Radio Out Of Tree (OOT) block capable of running deep learning inference inside GNU Radio flow graphs. This module integrates a deep
From playlist GRCon 2020
Unleashing the Power of BLOOM 176B with AWS ml.p4de.24xlarge, DJL & DeepSpeed: The Ultimate Boost!
More Power! How and where to run inference of an LLM w/ 176 billion parameter? Well, what about the most expensive ML instance on AWS? The most performant implementation for LLMs (utilizing latest .. and most expensive .. cloud infrastructure)? Some implementation ideas ... Regarding LLM
From playlist Large Language Models - ChatGPT, GPT-4, BioGPT and BLOOM LLM explained and working code examples
Regression as a first step in deep learning
This a series for healthcare professionals and anyone else interested in learning how to create deep neural networks. In this video tutorial I introduce some basic concepts with the help of the familiar topic of linear regression. The RPubs blog post is at: http://rpubs.com/juanhklopper/
From playlist Introduction to deep learning for everyone
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
Geoffrey Hinton: "Introduction to Deep Learning & Deep Belief Nets"
Graduate Summer School 2012: Deep Learning, Feature Learning "Part 1: Introduction to Deep Learning & Deep Belief Nets" Geoffrey Hinton, University of Toronto Institute for Pure and Applied Mathematics, UCLA July 9, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-sc
From playlist GSS2012: Deep Learning, Feature Learning
Tutorial on deep learning for causal inference
Speakers: Bernard Koch (SICSS-Los Angeles 19, 20, 21; Ph.D. student in Sociology at UCLA) Description: This tutorial will teach participants how to build simple deep learning models for causal inference. Although this literature is still quite young, neural networks have the potential to
From playlist All Videos
Lecture 14.5 — RBMs are infinite sigmoid belief nets [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
Lecture 14E : RBMs are Infinite Sigmoid Belief Nets
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 14E : RBMs are Infinite Sigmoid Belief Nets
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Ruslan Salakhutdinov: "Learning Hierarchical Generative Models, Pt. 2"
Graduate Summer School 2012: Deep Learning, Feature Learning "Learning Hierarchical Generative Models, Pt. 2" Ruslan Salakhutdinov, University of Toronto Institute for Pure and Applied Mathematics, UCLA July 23, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-school
From playlist GSS2012: Deep Learning, Feature Learning
Lecture 15 | Efficient Methods and Hardware for Deep Learning
In Lecture 15, guest lecturer Song Han discusses algorithms and specialized hardware that can be used to accelerate training and inference of deep learning workloads. We discuss pruning, weight sharing, quantization, and other techniques for accelerating inference, as well as parallelizati
From playlist Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)
