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
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
Transformer (Attention is all you need)
understanding Transformer with its key concepts (attention, multi head attention, positional encoding, residual connection label smoothing) with example. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning
From playlist Machine Learning Course
Introduction To Machine Learning | Machine Learning Basics for Beginners | ML Basics | Simplilearn
Machine Learning is a trending topic nowadays. This Introduction to Machine Learning video will help you to understand what is Machine Learning, importance of Machine Learning, advantages and disadvantages of Machine Learning, what are the types of Machine Learning - supervised, unsupervis
Machine Learning with scikit learn Part Two | SciPy 2017 Tutorial | Andreas Mueller & Alexandre Gram
Tutorial materials found here: https://scipy2017.scipy.org/ehome/220975/493423/ Machine learning is the task of extracting knowledge from data, often with the goal of generalizing to new and unseen data. Applications of machine learning now touch nearly every aspect of everyday life, fro
From playlist talks
Machine learning is all around us; on our phones, powering social networks, helping the police and doctors, scientists and mayors. But how does it work? In this animation we take a look at how statistics and computer science can be used to make machines that learn. Visit www.oxfordsparks
From playlist Machine Learning
Introduction To Autoencoders In Machine Learning.
Autoencoders are neural networks designed in a way they can learn any existing structure in a dataset. They create a compact representation of the data we can leverage later in different applications. Some applications where you can leverage autoencoders: anomaly detection, image denoisin
From playlist Machine Learning Techniques
Learning Automata with Hankel Matrices - Borja Balle, Amazon Research Cambridge
The Hankel matrix is a fundamental tool in the theory of weighted automata. In this talk we will describe a general framework for learning automata with Hankel matrices. Our framework provides a unified view of many classical and recent algorithms for learning automata under different lear
From playlist Logic and learning workshop
Lecture 7.3 — A toy example of training an RNN [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 7C : A toy example of training an RNN
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 7C : A toy example of training an RNN
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Machine learning describes computer systems that are able to automatically perform tasks based on data. A machine learning system takes data as input and produces an approach or solution to a task as output, without the need for human intervention. Machine learning is closely tied to th
From playlist Data Science Dictionary
What We've Learned from NKS Chapter 11: The Notion of Computation
In this episode of "What We've Learned from NKS", Stephen Wolfram is counting down to the 20th anniversary of A New Kind of Science with [another] chapter retrospective. If you'd like to contribute to the discussion in future episodes, you can participate through this YouTube channel or th
From playlist Science and Research Livestreams
What We've Learned from NKS Chapter 6: Starting from Randomness
In this episode of "What We've Learned from NKS", Stephen Wolfram is counting down to the 20th anniversary of A New Kind of Science with [another] chapter retrospective. If you'd like to contribute to the discussion in future episodes, you can participate through this YouTube channel or th
From playlist Science and Research Livestreams
1. Introduction, Finite Automata, Regular Expressions
MIT 18.404J Theory of Computation, Fall 2020 Instructor: Michael Sipser View the complete course: https://ocw.mit.edu/18-404JF20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60_JNv2MmK3wkOt9syvfQWY Introduction; course outline, mechanics, and expectations. Described
From playlist MIT 18.404J Theory of Computation, Fall 2020
What We've Learned from NKS Chapter 10: Processes of Perception and Analysis
In this episode of "What We've Learned from NKS", Stephen Wolfram is counting down to the 20th anniversary of A New Kind of Science with [another] chapter retrospective. If you'd like to contribute to the discussion in future episodes, you can participate through this YouTube channel or th
From playlist Science and Research Livestreams
Wolfram Student Podcast Episode 6: Computationally Generated Music Based on Lyrics
In our 6th episode of the Wolfram Student Podcast, we talk to Sohum Bajaj about his project on computationally generating music based on lyrics. Join us as we discuss how to computationally represent pitch and sentiment, and cellular automata’s role in music! If you would like to be feat
From playlist Wolfram Student Podcast
Machine Learning: Zero to Hero
This is a talk for people who know code, but who don’t necessarily know machine learning. Learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code. This session will guide you
From playlist Machine Learning