Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
Software Engineering, as a discipline, has matured over the past 5+ decades. The modern world heavily depends on it, so the increased maturity of Software Engineering was an eventuality. Practices like testing and reliable technologies help make Software Engineering reliable enough to build industries upon. Meanwhile, Machine Learning (ML) has also grown over the past 2+ decades. ML is used more a
Dustin Tran
I am a staff research scientist at Google DeepMind. I am broadly interested in intelligence under themes like programs and probability. Currently, I lead evaluation for Bard and also work on Gemini. My most notable works are in infrastructure (Mesh TensorFlow, Tensor2Tensor, TensorFlow Probability, Edward), modeling (Image Transformer, Automatic Differentiation Variational Inference), and evaluati
vision#list-of-materials
Welcome! Let’s get started The following instructions show you how to assemble your AIY Vision Kit, connect to it, and run the Joy Detector demo, which recognizes faces and detects if they're smiling. Then you can try running some other demos that detect other kinds of objects with the camera. You can even install your own custom-trained TensorFlow model. Time required to build: 1.5 hours You’ll n
Hello World - Machine Learning Recipes #1
Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up. Follow https://twitter.com/random_forests for updates on new episodes! Subscribe to the Google Developers:
Learn TensorFlow and deep learning, without a Ph.D. | Google Cloud Blog
This 3-hour course (video + slides) offers developers a quick introduction to deep-learning fundamentals, with some TensorFlow thrown into the bargain. ">This 3-hour course (video + slides) offers developers a quick introduction to deep-learning fundamentals, with some TensorFlow thrown into the bargain. Deep learning (aka neural networks) is a popular approach to building machine-learning models
Jeff Dean Talks Google Brain and Brain Residency
Watch Google's Jeff Dean talk about Google Brain and the Brain Residency Program. Jeff will will discuss the current state of the Google Brain Team, Tensorflow, the future directions of Brain and tell you a bit more about our Google Brain Residency Program (2017 application open now! https://goo.gl/Z4TtnQ). Check out g.co/brain for more info! The first part of the video gives an overview of the wo
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GitHub - tensorflow/model-card-toolkit: A toolkit that streamlines and automates the generation of model cards
GitHub - google-deepmind/jraph: A Graph Neural Network Library in Jax
GitHub - deepmind/graph_nets: Build Graph Nets in Tensorflow
GitHub - google/ml-metadata: For recording and retrieving metadata associated with ML developer and data scientist workflows.
The surprising impact of mask-head architecture on novel class segmentation
Graph Mining and Learning @ NeurIPS
https://dl.acm.org/doi/pdf/10.1145/3399579.3399868
Continuous Training for Production ML in the TensorFlow Extended (TFX) Platform | USENIX
http://www.sysml.cc/doc/2019/167.pdf
Machine Learning | Google for Developers
http://learningsys.org/nips17/assets/papers/paper_1.pdf
GitHub - kazunori279/TensorFlow-for-absolute-beginners: TensorFlow for absolute beginners: a 3-hour codelab for self-learning neural network basics without hard math.
Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source
Show and Tell: image captioning open sourced in TensorFlow
