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14
01:21:59

Probabilistic ML — Lecture 20 — Latent Dirichlet Allocation

This is the twentieth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig, updated for the Summer Term 2021 at the University of Tübingen. Slides available at https://uni-tuebingen.de/en/180804. Contents: * How to design probabilistic machine learning solutions * Latent Dirichlet Allocation * conditional independence (rejoinder) * Gibbs sampling (rejoinder) © Philipp Hennig / […]
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11
43:19

Causality 101 with Robert Ness – #342

Today we’re accompanied by Robert Osazuwa Ness, Machine Learning Research Engineer at ML Startup Gamalon and Instructor at Northeastern University. Robert, who we had the pleasure of meeting at the Black in AI Workshop at NeurIPS last month, joins us to discuss: Causality, what it means, and how that meaning changes across domains and users. […]
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586
09:50

Swift for TensorFlow (TensorFlow Meets)

It’s still the early days for Swift for TensorFlow, but Jeremy Howard is embracing the language for use in high performance numeric computing. On this episode of TensorFlow Meets, Josh (@random_forests) talks to the renowned data scientist and fast.ai founder about the future of Swift for TensorFlow, as well as the Swift online course coming […]
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19
04:06

When to Walk Away from a Gig | Machine Learning Freelancer Series | 2019

#freelancing #machinelearning As a machine learning freelancer, you may eventually have to walk away from a gig. Hear my story of what prompted me to walk away from my most recent gig. Learn how to turn deep reinforcement learning papers into code: Deep Q Learning: https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=DQN-OCT-21 Actor Critic Methods: https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-OCT-21 Curiosity Driven Deep Reinforcement Learning […]
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8
42:41

Language Modeling and Protein Generation at Salesforce with Richard Socher – #372

Today we’re joined Richard Socher, Chief Scientist and Executive VP at Salesforce. Richard, who has been at the forefront of Salesforce’s AI Research since they acquired his startup Metamind in 2016, and his team have been publishing a ton of great projects as of late, including CTRL: A Conditional Transformer Language Model for Controllable Generation, […]
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251
20:10

How To Deploy ML Models With Google Cloud Run

Learn how to deploy Machine Learning / Deep Learning models with Google Cloud Run. We build a simple app with TensorFlow and Flask, containerize it with Docker, and deploy it to Google Cloud Run. Code and instructions: https://github.com/python-engineer/ml-deployment Get my Free NumPy Handbook: https://www.python-engineer.com/numpybook 🪁 Code faster with Kite, AI-powered autocomplete that integrates into PyCharm […]
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25
58:19

PyTorch Community Voices | Catalyst| Sergey Kolesnikov

Join us for an interview with star PyTorch community member Sergey Kolesnikov, the creator of Catalyst, a high-level PyTorch framework for Deep Learning Research and Development (presentation slides below). It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop. With Catalyst, you get […]
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446
08:47

Giới thiệu về công nghệ học máy (Machine Learning: Zero to Hero, phần 1)

This video is also available in English → https://goo.gle/3sJoXSj Công nghệ học máy (machine learning) là một hình thức lập trình mới. Trong đó, thay vì lập trình các chỉ thị cho máy tính bằng ngôn ngữ lập trình như Java hoặc C++, thì với học máy, bạn sẽ tạo một chương trình được huấn […]
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7
40:36

CreaTech Stage: Creative industries on 5G

CreaTech Stage: Creative industries on 5G The high-bandwidth low-latency technologies promised by 5G are likely to radically disrupt the Creative Industries, transforming the production, distribution and consumption models as well as the content creation. In this session we explore the potential of 5G and hear about innovative trials, exciting projects and ideas for the future. […]
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17
01:02:00

Recitation 9 | Attention Mechanisms and Memory Networks

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 For more information, please visit: http://deeplearning.cs.cmu.edu/ Contents: • Attention Mechanisms and Memory Networks YouTube Source for this AI Video
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162
07:17

Single Number Evaluation Metric (C3W1L03)

Take the Deep Learning Specialization: http://bit.ly/2vIiFtZ Check out all our courses: https://www.deeplearning.ai Subscribe to The Batch, our weekly newsletter: https://www.deeplearning.ai/thebatch Follow us: Twitter: https://twitter.com/deeplearningai_ Facebook: https://www.facebook.com/deeplearningHQ/ Linkedin: https://www.linkedin.com/company/deeplearningai Source of this AI Video
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4
38:53

Fintech & Future Financial Services: Cashless world already tomorrow?

Fintech & Future Financial Services: Cashless world already tomorrow? Square and Checkout.com, two of the leading innovators in the payments industry, discuss the trends in the payments industry in terms of mobile payments, one-click payments and tokenisation, the growth of contactless payments, the revenue growth projections of electronic payments through e-commerce. Featuring: Kaushalya Somasundaram – […]
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12
17:05

Building Responsible AI at PyTorch

Join Jyothi Nookula (Product Manager at PyTorch, Facebook AI) and Narine Kokhlikyan (Research Scientist at Facebook AI) as they talk about Responsible AI. They’ll discuss what Responsible AI means, why it matters, and share tips and ideas for projects you can build this year at the 2021 PyTorch Annual Hackathon. Interested in joining our virtual […]
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4
44:06

Ethics & Society: SafetyTech: Tackling the toxic web

Ethics & Society: SafetyTech: Tackling the toxic web How can we create more safety in an online world of discrimination? Part of the answer is in bridging the gaps between technologists, policymakers, researchers and users. Never has this been more important for women, many of whom experience online abuse. This session will showcase how workshops, […]
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17
01:06:04

Probabilistic ML — Lecture 22 — Mixture Models

This is the twentysecond lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig, updated for the Summer Term 2021 at the University of Tübingen. Slides available at https://uni-tuebingen.de/en/180804. Contents: * Gaussian Mixture Models * the EM algorithm * Complete Data Log Likelihood © Philipp Hennig / University of Tübingen, 2021 CC BY-NC-SA 3.0 […]
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9
05:30

ICLR 2021: ResNet after all? Neural ODEs and their numerical solution

This is the video presentation for the ICLR 2021 publication ResNet after all? Neural ODEs and their numerical solution Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann in cooperation with the Bosch Center for Artificial Intelligence (https://www.bosch-ai.com) Paper: https://openreview.net/forum?id=HxzSxSxLOJZ Code: https://github.com/boschresearch/numerics_independent_neural_odes more about our research can be found at https://uni-tuebingen.de/en/134428 Source of this “Tübingen Machine […]
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14
01:32:22

Probabilistic ML — Lecture 25 — Customizing Probabilistic Models & Algorithms

This is the twenty-fifth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig in the Summer Term 2021 at the University of Tübingen. Time-stamped slides available at https://uni-tuebingen.de/en/180804. Contents: * collapsed variatonal bounds * modeling document meta-data and temporal structure * kernel topic models © Philipp Hennig / University of Tübingen, 2021 CC […]
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7
01:19:58

Probabilistic ML — Lecture 21 — Efficient Inference and k-Means

This is the twentyfirst lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig, updated for the Summer Term 2021 at the University of Tübingen. Slides available at https://uni-tuebingen.de/en/180804. Contents: * Collapsed Gibbs Sampling * k-Means * Lyapunov Functions © Philipp Hennig / University of Tübingen, 2021 CC BY-NC-SA 3.0 Source of this “Tübingen […]