Machine Learning1917 Videos

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07:16

I'm Addicted to Learning – TDBS 28 April 2017

I don’t know whether this is a good thing or a bad thing. Daily challenge: Learn something new! 😀 For more of me, check out: Web: www.mrdbourke.com Writing: www.mrdbourke.com/blog/ Instagram: https://www.instagram.com/mrdbourke/ Facebook: www.facebook.com/mrdbourke Twitter: www.twitter.com/mrdbourke Source of this AI Video
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55:41

TensorFlow.js: Make a smart webcam in JS with a pre-trained ML model | Workshop

Learn how to detect over 80 common objects in real time by using a TensorFlow.js pre-trained model in your web browser to give your next web application superpowers. We walk through an end-to-end creation of a smart camera in this Workshop. Resources: What’s new in TensorFlow.js? Machine learning for next gen web apps → https://goo.gle/3v6cwBg […]
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01:24:05

Lecture 13: Recurrent Networks

00:00 Introduction 00:14:55 Finite Response Model 00:23:00 Infinite Response System 00:30:00 NARX Network 00:34:30 Jordan Network 00:35:50 Elman Network 00:43:00 State Space Model 00:51:30 Recurrent Neural Network 00:59:10 Variants 01:04:30 Training the RNN 01:10:50 Backpropagation through RNN YouTube Source for this AI Video
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17:38

Face Generation with NVIDIA StyleGAN2-ADA PyTorch and Python 3 (7.3)

It can take considerable training effort and compute time to build a face-generating GAN from scratch. NVIDIA StyleGAN2-ADA PyTorch offers pretrained weights that allow you to generate realistic faces out of the box. StyleGAN does require a GPU, however, Google CoLab GPU works just fine, as this video demonstrates. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_07_3_style_gan.ipynb Course […]
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21
09:43

Try Going Over It Again | Learning Intelligence 3

Part 3 of my Learning Intelligence series! Previously, I’d had some trouble understanding some of the concepts in the Udacity AI Nanodegree. Rather than move forward, I decided to go over the material I had already gone over again. I don’t usually do this but I think I’ve found a new study method for myself. […]
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140
09:03

TensorFlow Hub: Making model discovery easy (TF Dev Summit '20)

TF Hub is the main repository for ML models. This talk looks into all the new features and how you can use Hub in your model discovery journey. Speaker: Sandeep Gupta – Product Manager Resources: TensorFlow Hub → https://goo.gle/32XwUY9 GitHub → https://goo.gle/3cGLdFc Neural style transfer → https://goo.gle/2VPlqEE Text classification with TensorFlow Hub → https://goo.gle/2VQZHMp Watch […]
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46:29

Computer Vision – Lecture 6.3 (Applications of Graphical Models: Optical Flow)

Lecture: Computer Vision (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/ Source of this “Tübingen Machine Learning” AI Video
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23:04

Kaggle Top 10%: Training Models for Ensembles

Learn to train a variety of models for Kaggle using my Kaggle utilities. These models can be ensembled together for better results. This is part of a series where I will discuss some techniques for placing in the top 10% that I’ve used in two Kaggle competitions using just a laptop and no cloud. I […]
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07:08

How to Burn Over 100 Calories in 4 Minutes

How to Burn Over 100 Calories in 4 Minutes TABATA training is one of my favourite methods of cardio. It may be short but it’s no walk in the park. Keep your rest time short (10 seconds) and make sure you go as hard as you can for each 20-second interval. TABATA Training (4 minutes) […]
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81
10:10

TFRT: A new TensorFlow runtime (TF Dev Summit '20)

TFRT is a new runtime for TensorFlow. Leveraging MLIR, it aims to provide a unified, extensible infrastructure layer with best-in-class performance across a wide variety of domain specific hardware. This approach provides efficient use of the multithreaded host CPUs, supports fully asynchronous programming models, and is focused on low-level efficiency. Speaker: Mingsheng Hong – Senior […]
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01:22:38

Lecture 10 | CNNs pt 2

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2020 For more information, please visit: https://deeplearning.cs.cmu.edu/ YouTube Source for this AI Video
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10:27

PyTorch and Keras TensorFlow for the Next 3 Years, Reflections from GTC 2021

Several presentations at GTC-21 discussed the next 3-5 year direction for PyTorch and Keras/Tensorflow. In this video, I summarize what is ahead for these important frameworks. Presentations referenced: François Chollet Soumith Chintala https://www.nvidia.com/en-us/gtc/topics/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton Tweets by jeffheaton Support Me on Patreon: https://www.patreon.com/jeffheaton Source of this machine learning/AI Video
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01:45

Functional Friday | Backyard Workout

One of my good friends Jay texted me this morning asking if I wanted to workout in my backyard. It was hot but I was also keen to get a pump. We decided to do a bit of circuit training with a bunch of functional exercises. All of the footage is shot on a DJI […]
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11:48

Enjoying the show – Gant Laborde – Made with TensorFlow.js

Welcome to the 1st episode of Made with TensorFlow.js by Jason Mayes, Developer Advocate for TensorFlow.js. Today, we’re joined by Gant Laborde from the #MadeWithTFJS community who explains how he solved a problem when presenting digitally to an audience where he was unable to know if they were interested in the content being presented. Learn […]
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02:13

Probabilistic Numerics for ODEs 8: ODE filters can efficiently solve Inverse Problems

This video is part of a ten-part spotlight series on Probabilistic Numerical Methods for (ordinary) differential equations. In this eighth video, Emilia Magnani presents work by Kersting et al. that shows probabilistic ODE filters provide differentials of the solution with respect to the vector field at essentially no cost (even less than explicit automatic differentiation […]
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08:37

Introduction to Neural Networks for Java(Class 5/16, Part 2/5) – genetic algorithm

Learn Neural Net Programming: http://www.heatonresearch.com/course/intro-neural-nets-java In class session 5, part 2 we will look at how to implement genetic algorithms. We will use genetic algorithms both to train a neural network and to provide a path for the traveling salesman problem. Artificial intelligence online course presented by Jeff Heaton, Heaton Research. Source of this machine […]
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15:16

Disagree and Commit, Words of Wisdom from Jeff Bezos – TDBS 19 April 2017

Disagree and Commit is a saying frequently used by Jeff Bezos. It’s a way of making decisions faster. You can far too long being in disagreement with someone for what it’s worth. I picked this up when reading his annual letter to Amazon customers, employees and shareholders. There’s a lot to learn from this very […]
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37:32

Inside TensorFlow: New TF Lite Converter

In this episode of Inside TensorFlow, Software Engineer Yu-Cheng Ling demonstrates to us the new TF Lite Converter. Let us know what you think about this presentation in the comments below and make sure to subscribe! TensorFlow Lite → https://goo.gle/2Wk5MPM TensorFlow Lite Converter → https://goo.gle/2YDCVFM TensorFlow MLIR → https://goo.gle/2Y6WnOd MLIR overview → https://goo.gle/3aKHfcC TF Ops […]