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39:57

Ethics & Society: We’re all developing now: AI and geopolitical inclusion

Ethics & Society: We’re all developing now: AI and geopolitical inclusion To what extent is the developing world “at the table” for AI policy, and what can be done to improve that? As the world transitions towards new models of AI governance, rebalancing traditional nation state power with transnational collaborations, what is the emergent role […]
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39:34

Deep Learning with Structured Data w/ Mark Ryan – #301

Today we are joined by Mark Ryan, author of Deep Learning with Structured Data, currently in the Manning Early Access Program (MEAP), due for publication in Spring 2020. While working on the Support team at IBM Data and AI, he saw that there was a lack of general structured data sets that people could apply […]
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01:32:30

DeepMind x UCL | Deep Learning Lectures | 7/12 | Deep Learning for Natural Language Processing

This lecture, by DeepMind Research Scientist Felix Hill, first discusses the motivation for modelling language with ANNs: language is highly contextual, typically non-compositional and relies on reconciling many competing sources of information. This section also covers Elman’s Finding Structure in Time and simple recurrent networks, the importance of context and transformers. In the second part, […]
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08:13

Training and Eating like a SPARTAN! Spartan Race Preparation

Zenith, Chris and I signed up for the upcoming Spartan Ultra in Brisbane on April 28 2018. The Spartan Ultra is an elite variation of the Spartan Race. 60+ obstacles over a distance of 50km through the Gold Coast hinterland – we can’t wait! OTHER EPISODES: 1. Beginning of the preparation (this one): https://youtu.be/0YUxK6g3U2M 2. […]
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11:19

How To Code A Neural Network From Scratch Part 3 – Activating a neuron

In part 3 we’ll take a look at activating the neuron with the sigmoid function. We also take care of encoding the “one hot” representation of our training data Code from this tutorial comes from this book (not an affiliate link): https://amzn.to/38wBIoX Learn how to turn deep reinforcement learning papers into code: Deep Q Learning: […]
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10:08

10 Python Basics You Should Know!

10 Must Know Python Basics and More Tips And Tricks. How many did you already know? Get my Free NumPy Handbook: https://www.python-engineer.com/numpybook ✅ Write cleaner code with Sourcery, instant refactoring suggestions in VS Code & PyCharm: https://sourcery.ai/?utm_source=youtube&utm_campaign=pythonengineer * 🪁 Code faster with Kite, AI-powered autocomplete that integrates into PyCharm & VSCode: https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only * ⭐ Join […]
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09:58

Computing Neural Network Output (C1W3L03)

Take the Deep Learning Specialization: http://bit.ly/38jCe9e 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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08:31

PyTorch/XLA Internal | PyTorch Developer Day 2020

PyTorch / XLA is a library that enables PyTorch code to run on CPUs, GPUs, and Cloud TPUs via the XLA linear algebra compiler. In this talk, Jack Cao, a software engineer at Google, provides an overview of PyTorch / XLA, as well as some technical details and an example walk-through. You can learn more […]
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18:59

1.3: Python Anaconda for Deep Learning, Keras and Tensorflow (Module 1, Part 3)

An introduction to Anaconda Python for deep learning with Keras and TensorFlow. An introduction is provided for vectors, dictionaries, and other Python language elements. This course is taught in a hybrid format at Washington University in St. Louis; however, all the information is online and you can easily follow along. T81-558: Application of Deep Learning, […]
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54:54

PyTorch Ecosystem Day 2021 – EMEA/US Opening Talks

The PyTorch Ecosystem Day is a virtual event designed for the PyTorch ecosystem and industry communities to showcase their work and discover new opportunities to collaborate. In this open sessions, you’ll have the opportunity to hear from the following PyTorch experts: *My Journey to PyTorch* 00:16-09:10: Piotr Bialecki – Technical Lead, PyTorch @NVIDIA *PyTorch Release* […]
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13:43

Torch for R & Hasktorch: Bringing Torch to New Programming Languages | PyTorch Developer Day 2020

This talk covers the development of libtorch-powered frameworks in languages beyond Python. Software engineer Daniel Falbel announces the release of Torch for R, an open source machine learning framework based on PyTorch. Then, Austin Huang, Vice President of AI & Machine Learning at Fidelity, introduces Hasktorch, a torch-powered framework for the Haskell programming language. Source […]
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01:00:39

ML-Powered Language Learning at Duolingo with Burr Settles – #412

Today we’re joined by Burr Settles, Research Director at Duolingo. Most would acknowledge that one of the most effective ways to learn is one on one with a tutor, and Duolingo’s main goal is to replicate that at scale. In our conversation with Burr, we dig how the business model has changed over time, the […]
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55:03

PyTorch Community Voices | PyTorch Profiler | Sabrina & Geeta

Join us for an interview with star PyTorch community members Sabrina Smai (Product Manager @ Microsoft) & Geeta Chauhan (AI/PyTorch Partner Engineering Head @ Facebook) as we learn about the newly released PyTorch Profiler v1.9, a tool that collects the performance metrics of machine learning models during the training and inference. 0:00 Starting soon 1:22 […]
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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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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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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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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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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 […]