Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning
Here we describe Q-learning, which is one of the most popular methods in reinforcement learning. Q-learning is a type of temporal difference learning. We discuss other TD algorithms, such as SARSA, and connections to biological learning through dopamine. Q-learning is also one of the mo
From playlist Reinforcement Learning
Introduction to Machine Learning with Time Series || Markus Loning
Time series are ubiquitous in real-world applications, but often add considerable complications to data science workflows. What’s more, most available machine learning toolboxes (e.g. scikit-learn) are limited to the tabular setting, and cannot easily be applied to time series data. In th
From playlist Machine Learning
In this video, you’ll learn more about the evolution of machine learning and its impact on daily life. Visit https://www.gcflearnfree.org/thenow/what-is-machine-learning/1/ for our text-based lesson. This video includes information on: • How machine learning works • How machine learning i
From playlist Machine Learning
How to Take Online Classes - Study Tips - Distance Learning
Are you taking classes online? Do you need help figuring out Distance Learning? Socratica Friends, we’re here to help. You don’t have to be in a classroom to be a Great Student. But there are some important differences about the best ways to study for Online Courses. One big difference
From playlist It Starts With Literacy
Reinforcement Learning: Temporal Difference - Session 6
Temporal difference: combining Monte Carlo (MC) and Dynamic Programming (DP) Advantages of TD No environment model required (vs DP) Continual updates (vs MC) Example: reinforcers
From playlist Reinforcement Learning
Knowing Ourselves Intellectually vs. Knowing Ourselves Emotionally
It's obviously a great idea to try to understand ourselves, but one of the further distinctions we need to make is between knowing ourselves intellectually and knowing ourselves emotionally. The latter variety of knowledge is rather harder but a good deal more valuable as well. For gifts a
From playlist SELF
From playlist Machine Learning Course
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 Machine Learning
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
Lecture 05: Temporal-Difference Learning
Fifth lecture video on the course "Reinforcement Learning" at Paderborn University during the summer term 2020. Source files are available here: https://github.com/upb-lea/reinforcement_learning_course_materials
From playlist Reinforcement Learning Course: Lectures (Summer 2020)
Temporal Difference Learning - Reinforcement Learning Chapter 6
Free PDF: http://incompleteideas.net/book/RLbook2018.pdf Print Version: https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262039249/ref=dp_ob_title_bk Please Check out Chapter 1 in this Series! https://www.youtube.com/watch?v=4SLGEq_HZxk Thanks for watch
From playlist Reinforcement Learning
n-step Bootstrapping - Reinforcement Learning Chapter 7!
Free PDF: http://incompleteideas.net/book/RLbook2018.pdf Print Version: https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262039249/ref=dp_ob_title_bk Please check out Chapter 1 in the Series! https://www.youtube.com/watch?v=4SLGEq_HZxk Thanks for watchi
From playlist Reinforcement Learning
DeepMind x UCL RL Lecture Series - Model-free Prediction [5/13]
Research Scientist Hado van Hasselt takes a closer look at model-free prediction and its relation to Monte Carlo and temporal difference algorithms. Slides: https://dpmd.ai/modelfreeprediction Full video lecture series: https://dpmd.ai/DeepMindxUCL21
From playlist Learning resources
Remembering and the Brain: Can Brain Scans Detect Memories?
(October 23, 2009) Stanford Professor of psychology and neuroscience, Anthony Wagner PhD, discusses how the brain supports memory for everyday events, and will evaluate whether "mind reading" with brain imaging can detect when a person remembers the past and how this might be used as evide
From playlist Reunion Homecoming
Make-A-Video: Text-To-Video Generation Without Text-Video Data | Paper Explained
🚀 Find out how to get started using Weights & Biases 🚀 http://wandb.me/ai-epiphany 👨👩👧👦 Join our Discord community 👨👩👧👦 https://discord.gg/peBrCpheKE In this video I cover the latest text-to-video paper from Meta: "Make-A-Video: Text-To-Video Generation Without Text-Video Data". I
From playlist Video
Deep Reinforcement Learning of Marked Temporal Point Processes by Abir De
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)
Reinforcement Learning 4: Model-Free Prediction and Control
Hado van Hasselt, Research Scientist, discusses model-free prediction and controls as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
From playlist DeepMind x UCL | Reinforcement Learning Course 2018
AI Weekly Update #11 - November 4th, 2019
https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning https://www.microsoft.com/en-us/research/blog/pipedream-a-more-effective-way-to-train-deep-neural-networks-using-pipeline-parallelism/ https://ai.googleblog.com/2019/10/le
From playlist AI Research Weekly Updates