#TWIMLfest: Office Hours – Reinforcement Learning

In the Office Hours series, we invite experts and practitioners in various topic areas for AMA (ask-me-anything) style sessions to answer community member questions. The intent is to answer technical questions and/or help participants advance their specific projects and interests. This week’s topic will be centered on Reinforcement Learning! Resources: Show notebooks in Drive – […]

Social Commonsense Reasoning with Yejin Choi – 518

Today we’re joined by Yejin Choi, a professor at the University of Washington. We had the pleasure of catching up with Yejin after her keynote interview at the recent Stanford HAI “Foundational Models” workshop. In our conversation, we explore her work at the intersection of natural language generation and common sense reasoning, including how she […]

Protecting Front line American Healthcare Workers Fighting COVID19: Lessons from South Korea

This event is a Q&A session with Dr. Doo Ryeon Chung, MD PhD, Director of Infection Prevention and Control at Samsung Medical Center in Seoul, South Korea. He will be sharing key lessons and strategies for preventing COVID19 transmission within hospitals, including PPE standards, workflows, infrastructure, and workforce management. The webinar is hosted by: Ron […]

Heroes of NLP: Chris Manning

Heroes of NLP is a video interview series featuring Andrew Ng, the founder of DeepLearning.AI, in conversation with thought leaders in NLP. Watch Andrew lead an enlightening discourse around how these industry and academic experts started in AI, their previous and current research projects, how their understanding of AI has changed through the decades, and […]

Machine Learning Engineering for Production (MLOps)

Welcome to our event celebrating the launch of Machine Learning Engineering for Production (MLOps) Specialization featuring AI leaders in MLOps. Topics we plan to cover: -To what extent does the role of Data Scientist or MLE involve MLOps? -How is MLOps actually implemented in an industry setting? Is there some kind of a framework people […]

Break into NLP hosted by deeplearning.ai

Welcome to Break into NLP – a virtual live event! NLP is an essential part of the practical application of AI. To celebrate the launch of Sequence Models, Course 3 of Natural Language Processing – a deeplearning.ai Specialization – we’ve assembled a panel of experts in the NLP field. They’re going to talk about their […]

Teaching TensorFlow for Deep Learning at Stanford University (TensorFlow Meets)

TensorFlow Meets Chip Huyen (@chipro), author and instructor of the TensorFlow for Deep Learning class at Stanford University: https://goo.gl/rNb6PW. They discuss the class, her journey from writing travel stories, to studying computer science, to now teaching students about deep learning at Stanford University! Remember this show is about YOU! We’d love to learn about your […]

Machine Learning: A New Approach to Drug Discovery with Daphne Koller – #332

Today we continue our 2019 NeurIPS coverage joined by Daphne Koller, co-Founder and former co-CEO of Coursera and Founder and CEO of Insitro. We caught up with Daphne to discuss: Her background in machine learning, beginning in ‘93, and her work with the Stanford online machine learning courses, and eventually her work at Coursera. The […]

Intro to Deep Learning (ML Tech Talks)

An overview of Deep Learning, including representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow. This talk is part of a ML speaker series we recorded at home. You can find all the links from this video below. I […]

Geometric Statistics in Machine Learning w/ geomstats with Nina Miolane – TWiML Talk #196

In this episode we’re joined by Nina Miolane, researcher and lecturer at Stanford University. Nina and I recently spoke about her work in the field of geometric statistics in machine learning. Specifically, we discuss the application of Riemannian geometry, which is the study of curved surfaces, to ML. Riemannian geometry can be helpful in building […]

Trends in Reinforcement Learning with Chelsea Finn – #335

Today we continue to review the year that was 2019 via our AI Rewind series, and do so with friend of the show Chelsea Finn, Assistant Professor in the Computer Science Department at Stanford University. Chelsea’s research focuses on Reinforcement Learning, so we couldn’t think of a better person to join us to discuss the […]

StanfordNLP – Yuhao Zhang, Stanford

StanfordNLP is a new Python natural language processing analysis package built on PyTorch that’s designed to process many human languages. Learn more about the StanfordNLP library in this talk. Source of this PyTorch AI Video

Designing Computer Systems for Software with Kunle Olukotun – TWiML Talk #211

Today we’re joined by Kunle Olukotun, Professor in the department of Electrical Engineering and Computer Science at Stanford University, and Chief Technologist at Sambanova Systems. Kunle was an invited speaker at NeurIPS this year, presenting on “Designing Computer Systems for Software 2.0.” In our conversation, we discuss various aspects of designing hardware systems for machine […]

PyTorch RNN Tutorial – Name Classification Using A Recurrent Neural Net

Implement a Recurrent Neural Net (RNN) from scratch in PyTorch! I briefly explain the theory and different kinds of applications of RNNs. Then we implement a RNN to do name classification. This tutorial is closely oriented on the following article: https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html ~~~~~~~~~~~~~~ GREAT PLUGINS FOR YOUR CODE EDITOR ~~~~~~~~~~~~~~ 🪁 Code faster with Kite: https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only […]

Gunnar Carlsson Interview – Topological Data Analysis

The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. My guest for this show is Gunnar Carlsson, professor emeritus of mathematics at Stanford University and president and co-founder of machine learning startup Ayasdi. Gunnar joined me after his session at the conference […]

GANs for Good- A Virtual Expert Panel by DeepLearning.AI

Welcome to GANs for Good- a virtual expert panel hosted by DeepLearning.AI! To celebrate the launch of GANs Specialization, we’ve assembled a panel of GANs experts. They will discuss some of their current projects and the importance and future of GANs and also provide practical career advice for ML practitioners. Agenda: PDT (*subject to change) […]

PyTorch Tutorial – RNN & LSTM & GRU – Recurrent Neural Nets

Implement a Recurrent Neural Net (RNN) in PyTorch! Learn how we can use the nn.RNN module and work with an input sequence. I also show you how easily we can switch to a gated recurrent unit (GRU) or long short-term memory (LSTM) RNN. ~~~~~~~~~~~~~~ GREAT PLUGINS FOR YOUR CODE EDITOR ~~~~~~~~~~~~~~ 🪁 Code faster with […]

How to Deploy a Machine Learning Model to Google Cloud for 20% Software Engineers (CS329s tutorial)

It’s time to reveal the magician’s secrets behind deploying machine learning models! In this tutorial, I go through an example machine learning deployment scenario using Google Cloud and an image recognition app called Food Vision 🍔👁. Get all the code on GitHub – https://github.com/mrdbourke/cs329s-ml-deployment-tutorial Slides – https://github.com/mrdbourke/cs329s-ml-deployment-tutorial/blob/main/CS329s-deploying-ml-models-tutorial.pdf Full CS329s syllabus – https://stanford-cs329s.github.io/index.html Learn ML (my […]

Snorkel: A System for Fast Training Data Creation with Alex Ratner – TWiML Talk #270

Today we’re joined by Alex Ratner, Ph.D. student at Stanford. In our conversation, we discuss: • Snorkel, the open source framework that is the successor to Stanford’s Deep Dive project. • How Snorkel is used as a framework for creating training data with weak supervised learning techniques. • Multiple use cases for Snorkel, including how […]

PyTorch Tutorial 14 – Convolutional Neural Network (CNN)

New Tutorial series about Deep Learning with PyTorch! ⭐ Check out Tabnine, the FREE AI-powered code completion tool I use to help me code faster: https://www.tabnine.com/?utm_source=youtube.com&utm_campaign=PythonEngineer * In this part we will implement our first convolutional neural network (CNN) that can do image classification based on the famous CIFAR-10 dataset. We will learn: – Architecture […]

PyTorch Developer Conference 2019 | Full Livestream

Watch the full set of talks from the 2019 PyTorch Developer Conference. Deep dive on PyTorch 1.3 and new tools and libraries including PyTorch Mobile, CrypTen, Captum, Detectron2 and more. Hear from AI researchers and engineers from leading organizations in academia and industry on how they’re using PyTorch for both cutting edge research and production. […]