Dyad X Machina is a research partnership that combines affective neuroscience and deep learning. In this episode, Laurence meets with the co-founder, Haohan Wang, who explains Dyad’s mission as bringing emotion into machine learning.
Watch to learn more about the intersection of deep learning and affective computing and Haohan’s four P’s of learning.
Machine learning models, especially deep learning ones, can be complex.
In this video from QCon.ai 2018, Chi Zeng walks us through how to debug, monitor, and examine the decisions of a TensorFlow-based model using the TensorBoard suite of visualizations.
Chi Zeng works on the TensorBoard suite of visualizations within Google Brain.
TensorBoard is a suite of visualization tools that make it easier to understand, debug, and optimize TensorFlow programs.
Developer Advocate Laurence Moroney speaks with Justine Tunney, the dev lead for TensorBoard, about how the debugger plugin gives you an x-ray into your models.
Siraj Raval demonstrates how to use TensorFlow to track people’s facial expressions and body positions with a regular webcam.
Siraj Raval explores the use of AI in classifying diseases in medical imagining.
Siraj Raval explains TensorFlow.js, Google’s deep learning framework’s port to JavaScript and what that means for browser based apps (and privacy).
Here’s an interesting interview with Josh Dillon, who works on Tensorflow.
In this video, he discusses working on the Distribution API, which is based on probabilistic programming. Watch this video to find out what exactly probabilistic programming is, where the use of Distributions and Bijectors comes into play, & how you can get started. Subscribe to our channel to stay up to date with Google Developers.
This week James Montemagno is joined by Jim Bennett, a Cloud Developer Advocate at Microsoft, who shows us how to use AI inside a mobile app to identify his daughters’ toys.
In the video below, he walks through using the Azure custom vision service to generate a model to identify different toys, then shows how you can use these models from inside your app, both remotely by calling an Azure service, or locally by running the model on your device using CoreML and Tensorflow.
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TensorFlow is a powerful data flow-oriented machine learning framework developed by Google’s Brain Team. It was designed to be easy to use and widely applicable on both numeric, neural network-oriented problems as well as other domains.
Here’s a video from Google introducing the techniques you can use to represent features – including Bucketing, Crossing, Hashing, and Embedding – and other utilities TensorFlow uses to help you create features.
The video is worth a watch just for the walkthrough of using TensorFlow Estimators to classify structured data.
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