Computer Literacy - (unit 2) - data and files - 2 of 7
Second unit of a series for newbie computer users. See http://proglit.com/computer-skills/ for additional information and material.
From playlist Computer Literacy - (unit 2) - data and files
Memory & Storage: Crash Course Computer Science #19
Pre-order our limited edition Crash Course: Computer Science Floppy Disk Coasters here! https://store.dftba.com/products/computer-science-coasters So we’ve talked about computer memory a couple times in this series, but what we haven’t talked about is storage. Data written to storage, like
From playlist Computer Science
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist CRISPR
Jorge Nocedal: "Tutorial on Optimization Methods for Machine Learning, Pt. 2"
Graduate Summer School 2012: Deep Learning, Feature Learning "Tutorial on Optimization Methods for Machine Learning, Pt. 2" Jorge Nocedal, Northwestern University Institute for Pure and Applied Mathematics, UCLA July 19, 2012 For more information: https://www.ipam.ucla.edu/programs/summ
From playlist GSS2012: Deep Learning, Feature Learning
What are the Two Important Types of Logarithms ( Log Base e and Log Base 10 ) : Logarithms, Lesson 3
This tutorial explains what how log base ten and log base e ( the natural log ) are represented. Join this channel to get access to perks: https://www.youtube.com/channel/UCn2SbZWi4yTkmPUj5wnbfoA/join :)
From playlist All About Logarithms
GCSE Upper and Lower Bounds Introduction Measures of Accuracy
www.m4ths.com GCSE and A Level Worksheets, videos and helpbooks. Full course help for Foundation and Higher GCSE 9-1 Maths All content created by Steve Blades
From playlist GCSE Upper and Lower Bounds
Introduction to Logarithms (Part 1) | Logarithms | A-Level Maths Series
A video revising the techniques and strategies for using logarithms (A-Level Maths). This video is part of the Logarithms and Exponentials module in A-Level maths, see my other videos below to continue with the series. Don’t forget to check this video out first: Negative and Fractional
From playlist A-Level Maths Series - Pure Mathematics
Recorded: Spring 2014 Lecturer: Dr. Erin M. Buchanan Materials: created for Memory and Cognition (PSY 422) using Smith and Kosslyn (2006) Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdoom.com/page/other-courses/
From playlist PSY 422 Memory and Cognition with Dr. B
Random Matrix Advances in Machine Learning - Couillet - Workshop 3 - CEB T1 2019
Romain Couillet (Université de Grenoble) / 01.04.2019 Random Matrix Advances in Machine Learning. Machine learning algorithms, starting from elementary yet popular ones, are difficult to theoretically analyze as (i) they are data-driven, and (ii) they rely on non-linear tools (kernels,
From playlist 2019 - T1 - The Mathematics of Imaging
Optimization method | Neural Style Transfer #3
❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The third video in the neural style transfer series! 🎨 You'll learn about: ✔️ The optimization-based (original Gatys et al.) NST method. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ✅ GitHub code: https://
From playlist Neural Style Transfer
CS231n Lecture 6 - Neural Networks Part 3 Intro to ConvNets
Training Neural Networks Part 2: parameter updates, ensembles, dropout Convolutional Neural Networks: intro
From playlist CS231N - Convolutional Neural Networks
Open Source vs. Closed Source Software
In this video, you’ll learn more about the differences between open-source software and closed-source software. Visit https://edu.gcfglobal.org/en/basic-computer-skills/ for more technology, software, and computer tips. We hope you enjoy!
From playlist Technology Trends
Harvard AM205 video 4.9 - Quasi-Newton methods
Harvard Applied Math 205 is a graduate-level course on scientific computing and numerical methods. The previous video in this series discussed using the Newton method to find local minima of a function; while this method can be highly efficient, it requires the exact Hessian of the functio
From playlist Optimizers in Machine Learning
Jorge Nocedal: "Tutorial on Optimization Methods for Machine Learning, Pt. 3"
Graduate Summer School 2012: Deep Learning, Feature Learning "Tutorial on Optimization Methods for Machine Learning, Pt. 3" Jorge Nocedal, Northwestern University Institute for Pure and Applied Mathematics, UCLA July 18, 2012 For more information: https://www.ipam.ucla.edu/programs/summ
From playlist GSS2012: Deep Learning, Feature Learning
Lec 12 | MIT Finite Element Procedures for Solids and Structures, Nonlinear Analysis
Lecture 12: Demonstrative example solutions in static analysis Instructor: Klaus-Jürgen Bathe View the complete course: http://ocw.mit.edu/RES2-002S10 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT Nonlinear Finite Element Analysis
Neural Networks Demystified [Part 6: Training]
After all that work it's finally time to train our Neural Network. We'll use the BFGS numerical optimization algorithm and have a look at the results. Supporting Code: https://github.com/stephencwelch/Neural-Networks-Demystified Yann Lecun's Efficient BackProp Paper: http://yann.lecun
From playlist Neural Networks Demystified
E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor.... by Anil Yelundur
DISCUSSION MEETING THE THEORETICAL BASIS OF MACHINE LEARNING (ML) ORGANIZERS: Chiranjib Bhattacharya, Sunita Sarawagi, Ravi Sundaram and SVN Vishwanathan DATE : 27 December 2018 to 29 December 2018 VENUE : Ramanujan Lecture Hall, ICTS, Bangalore ML (Machine Learning) has enjoyed tr
From playlist The Theoretical Basis of Machine Learning 2018 (ML)
NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Vowpal Wabbit Tutorial
Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Tutorial: Vowpal Wabbit by John Langford Abstract: We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features,\
From playlist NIPS 2011 Big Learning: Algorithms, System & Tools Workshop
When is a logarithm undefined? log4 (-32)
👉 Learn all about the properties of logarithms. The logarithm of a number say a to the base of another number say b is a number say n which when raised as a power of b gives a. (i.e. log [base b] (a) = n means that b^n = a). The logarithm of a negative number is not defined. (i.e. it is no
From playlist Rules of Logarithms