A Keras implementation of “A Simple Framework for Contrastive Learning of Visual Representations” (SimCLR)https://t.co/RUsFe4CQNLhttps://t.co/d87F44gLpn pic.twitter.com/AjfBl8cwxF
— hardmaru (@hardmaru) July 28, 2020
A Keras implementation of “A Simple Framework for Contrastive Learning of Visual Representations” (SimCLR)https://t.co/RUsFe4CQNLhttps://t.co/d87F44gLpn pic.twitter.com/AjfBl8cwxF
— hardmaru (@hardmaru) July 28, 2020
🔔 Update!
— TensorFlow (@TensorFlow) July 27, 2020
TensorFlow 2.3 has been released with new tools to make it easier to load and preprocess data, and solve input-pipeline bottlenecks. Check out all the latest features and updates in the blog.
Learn more ↓ https://t.co/yDXoDxp5EE
Guide to Keras preprocessing layers. Create end-to-end models capable of directly handling raw text, images, and structured data. Less worry about training/serving skewhttps://t.co/UjVTfIAKgW
— François Chollet (@fchollet) July 27, 2020
I published a guide to Keras preprocessing layers -- key new feature of the TensorFlow 2.3 release https://t.co/UjVTfIAKgW pic.twitter.com/CvQoHYf9rS
— François Chollet (@fchollet) July 25, 2020
Accelerating TF Lite 🏁⚡️
— TensorFlow (@TensorFlow) July 24, 2020
See how integration of the XNNPACK library with TensorFlow Lite improves neural network inference performance by 2.3X on average.
Learn more ↓ https://t.co/kWb9i5EgZd
Quantization Aware Training 📝
— TensorFlow (@TensorFlow) July 23, 2020
In this TF Model Optimization Toolkit tutorial, you’ll learn the fundamentals of quantization aware training, how to use the TF Keras API, and internals of implementation.
Let’s get started → https://t.co/IX7eYA0Cto pic.twitter.com/dTNOsfdASX
🏅#TFCommunitySpotlight Winner: Javier Gamazo Tejero 🏅
— TensorFlow (@TensorFlow) July 22, 2020
Javier used TF to capture movement with a webcam and transfer it to Google Street View to give a virtual experience of walking through different cities. Great work, Javier!
Javier’s GitHub → https://t.co/jxtLx847vc pic.twitter.com/Ohq39BA2Sv
This👇🏾 make it all worth it.@kaggle GM @ChrisDeotte used @RAPIDSai tsne, knn, & kmeans + @TensorFlow CNN in a complex pipeline (no bugs nor workarounds💪🏾) to find similar images compare train and test distributions and more https://t.co/ZQPqMCC9Nt https://t.co/Zeo5RQfSgR 🔥🔥🔥
— Joshua Patterson (@datametrician) July 18, 2020
A pair of new guides:
— Josh Gordon (@random_forests) July 17, 2020
Intro to graphs & functions: https://t.co/pMNyToa1Rq
Advanced autodiff: https://t.co/6pdPQweFnu
TF tweetorial: if you have a list of paths (strings) to mp3 files on disk, and a list of labels (integers) for these files, here's how you'd make a labeled audio dataset (supporting buffering and prefetching, and robust against corrupted files)
— François Chollet (@fchollet) July 15, 2020
Note: tfio is tensorflow-io pic.twitter.com/xIFp91RIzG
New tutorial!🚀
— Adrian Rosebrock (@PyImageSearch) July 13, 2020
Implementing basic R-CNN object detector from scratch with #Keras and #TensorFlow 2:
- Train on your own datasets
- Detailed tutorial
- Includes #Python codehttps://t.co/vlxyGoQuO7 👍#DeepLearning #MachineLearning #ArtificialIntelligence #AI #DataScience pic.twitter.com/SFPER6aPqv
Want to train your Keras models on a beefy machine on GCP (or just many machines)? With TensorFlow Cloud, you can just add a single line to your script or notebook and get it running -- no config work necessary past the initial setup.
— François Chollet (@fchollet) July 9, 2020
Try it: https://t.co/49NPl0YFvs pic.twitter.com/C0W6bFFwcm