Building a machine learning app | 10 hour coding livestream

Daniel Bourke · Beginner ·📰 AI News & Updates ·4y ago
Time to celebrate 100k subscribers! And what better way to do it than with a coding livestream? Tune in for 10 hours straight (give or take a bit for dancing breaks/food etc). Get the code - https://github.com/mrdbourke/food-not-food Try the app - https://www.foodnotfood.app Get the free stuff/surprises here - https://www.mrdbourke.com/100k-subs/ Links: Learn ML (beginner-friendly courses I teach) - https://www.mrdbourke.com/ml-courses/ ML courses/books I recommend - https://www.mrdbourke.com/ml-resources/ Read my novel Charlie Walks - https://www.charliewalks.com Connect elsewhere: Web - https://dbourke.link/web Twitter - https://www.twitter.com/mrdbourke Twitch - https://www.twitch.tv/mrdbourke ArXiv channel (past streams) - https://dbourke.link/archive-channel Get email updates on my work - https://dbourke.link/newsletter Timestamps: 0:00 - Intro & hello 3:53 - Stream rules (10 pushups, squats, kicks, punches every hour) 5:00 - What we're going to build (food not food app) 10:58 - First commit 21:52 - Collecting a dataset (food and not food images) 2:20:30 - Food break 2:33:59 - Back to coding 3:36:09 - Downloading images 3:55:25 - Small model building 4:47:59 - Making train and test datasets 4:56:18 - Building binary classification model 5:29:54 - First model training 5:54:34 - Filtering food and non-food images from ImageNet 7:52:39 - Machine learning model deployment with TensorFlow Lite and TensorFlow JS 9:16:44 - Testing predictions with TensorFlow Lite model 9:40:39 - First prediction working on deployed model!!! 10:04:50 - Model deployed and live to the internet!!!
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Chapters (17)

Intro & hello
3:53 Stream rules (10 pushups, squats, kicks, punches every hour)
5:00 What we're going to build (food not food app)
10:58 First commit
21:52 Collecting a dataset (food and not food images)
2:20:30 Food break
2:33:59 Back to coding
3:36:09 Downloading images
3:55:25 Small model building
4:47:59 Making train and test datasets
4:56:18 Building binary classification model
5:29:54 First model training
5:54:34 Filtering food and non-food images from ImageNet
7:52:39 Machine learning model deployment with TensorFlow Lite and TensorFlow JS
9:16:44 Testing predictions with TensorFlow Lite model
9:40:39 First prediction working on deployed model!!!
10:04:50 Model deployed and live to the internet!!!
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