Network Science 2021 @ HSE http://www.leonidzhukov.net/hse/2021/networks/
From playlist Network Science, 2021
Introduction to Classification Models
Ever wonder what classification models do? In this quick introduction, we talk about what classifications models are, as well as what they are used for in machine learning. In machine learning there are many different types of models, all with different types of outcomes. When it comes t
From playlist Introduction to Machine Learning
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
6.2 deep neural network models
From playlist Deep-Learning-Course
How to Model Custom Physical Components in Simscape
Simscape™ extends the MATLAB® language with constructs for modeling implicit equations. Learn more about Simscape: http://goo.gl/Jhsth7 Get a free Product Trial: https://goo.gl/5NvCdU Download Sample Lift Table Model: http://goo.gl/k4fYwA These extensions of MATLAB are used to model a tra
From playlist Physical Modeling
Grid Network Solution - 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
11.3 Neuroevolution Flappy Bird with TensorFlow.js
In this video, I take another pass at the Neuroevolution Flappy Bird coding challenge and replace my JavaScript vanilla neural network library with the TensorFlow.js Layers API. 💻https://github.com/CodingTrain/website/tree/master/Courses/natureofcode/11.3_neuroevolution_tfjs.js 🔗 TensorF
From playlist 11: Neuroevolution - The Nature of Code
Coding Challenge #158: Shape Classifier Neural Network with ml5.js
In this challenge, I demonstrate the entire process of training and deploying a machine learning classification model in JavaScript -- data collection, model training, and prediction! 💻 Code: https://thecodingtrain.com/CodingChallenges/158-shape-classifier.html 🎥 ml5.js: DoodleNet: https:
From playlist Beginners Guide to Machine Learning in JavaScript
Style Transfer using Spell with Yining Shi
In this live stream, Yining Shi demonstrates how to train a "Style Transfer Model" using Spell (Sign up here: https://spell.run/codingtrain). After training the model, Yining writes code to process new images in the browser with ml5.js. #ThisDotStyle #StyleTransfer #MachineLearning This s
From playlist Machine Learning with TensorFlow, ml5.js, and Spell
COVID Detection Using Deep Learning | COVID Detection With X-Rays | Deep Learning Training | Edureka
🔥 Deep Learning Training - TensorFlow Certification(𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎): https://www.edureka.co/ai-deep-learning-with-tensorflow 🔹You can find the code here : https://bit.ly/33WkmSl 🔹 This Edureka video on "𝐂𝐎𝐕𝐈𝐃 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 " will provide you with a comprehensive and de
From playlist Deep Learning With TensorFlow Videos
Kernel Recipes 2022 - io_uring: path to zerocopy I/O
With I/O devices getting faster each year, memory copies are becoming more and more expensive, wasting a lot of CPU cycles and being a burden to the memory subsystem. The problem goes even deeper, as device-to-device transfers usually require intermediate steps, pumping the data through th
From playlist Kernel Recipes 2022
Live Stream #176: Simple 2D Black Hole Simulation and NeuroEvolution with TensorFlow.js
To try the Brilliant stacks and queues quiz, go to https://brilliant.org/TheCodingTrainStack/ and sign up for free. The first 200 people that go to that link will get 20% off the annual Premium subscription. Black hole guide by Chris Orban and STEM Coding: https://www.asc.ohio-state.edu/o
From playlist Live Stream Archive
Imagination-Augmented Agents for Deep Reinforcement Learning
Commentary of https://arxiv.org/abs/1707.06203 Abstract We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods
From playlist Reinforcement Learning
CMU Neural Nets for NLP 2017 (9): Attention
This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * Attention * What do We Attend To? * Improvements to Attention * Specialized Attention Varieties * A Case Study: "Attention is All You Need" Slides: http://phontron.com/class/nn4nlp2017/assets
From playlist CMU Neural Nets for NLP 2017
Training convolutional neural network for self-driving - Python plays GTA p.11
Welcome to Part 11 of the Python Plays: Grand Theft Auto V tutorial series, where we're working on creating a self-driving car in the game. Leading up to this point, we've built a training dataset that consists of 80x60 resized game imagery data, along with keyboard inputs for A,W, and D
From playlist Python Plays: Grand Theft Auto V
Neural Network Fundamentals (Part 2): Classification and Regression
From http://www.heatonresearch.com. In this part we will see how to use classification and regression to represent data to a neural network. This part will focus on classification.
From playlist Neural Networks by Jeff Heaton