31
New
The latest blog posts to take off on Hacker News. 326 posts.
32
A friendly Introduction to Backpropagation in Python
My aim here is to test my understanding of Karpathy’s great blog post “Hacker’s guide to Neural Networks” as well as of Python, to get a hang of which I rece...
33
TensorFlow Feature Columns
Welcome to Part 2 of a blog series that introduces TensorFlow Datasets and Estimators. We're devoting this article to —a data structure describing the features that an Estimator requires for training and inference. As you'll see, feature columns are very rich, enabling you to represent a diverse range of data.
34
A Cookbook for Machine Learning: Vol 1
This was a busy week, I had no time to read anything new, so I'm sharing a note that I wrote for myself, for no other reason than to understand things better. It's a kind of cookbook of various "transformations" you can apply to...
35
An On-Device Deep Neural Network for Face Detection
Apple started using deep learning for face detection in iOS 10. With the release of the Vision framework, developers can now use this…
36
SLING: A Natural Language Frame Semantic Parser
Posted by Michael Ringgaard, Software Engineer and Rahul Gupta, Research ScientistUntil recently, most practical natural language understanding (NL...
37
A Guide to Natural Language Processing
A guide to Natural Language Processing (NLP): how to analyze text, extract information and understand documents written in a natural language
38
39
Augmented reality with Python and OpenCV
You may (or may not) have heard of or seen the augmented reality Invizimals video game or the Topps 3D baseball cards. The main idea is to render in the screen of a tablet, PC or smartphone a 3D mo…
40
41
42
43
Gaussian Distributions Are Soap Bubbles
This post is just a quick note on some of the pitfalls we encounter when dealing with high-dimensional problems, even when working with something as simple as a Gaussian distribution. Last week I wrote about mixup [https://www.inference.vc/mixup-data-dependent-data-augmentation/] a new data-augmentation...
44
Capsule Networks Explained
Assumed Knowledge: Convolutional Neural Networks, Variational Autoencoders Disclaimer: This article does not cover the mathematics behind Capsule Networks, but rather the intuition and motivation behind them. What are Capsule Networks and why do they exist? The Capsule Network is a new type of neural network…
45
46
Fused Video Stabilization on the Pixel 2 and Pixel 2 XL
Posted by Chia-Kai Liang, Senior Staff Software Engineer and Fuhao Shi, Android Camera TeamOne of the most important aspects of current smartphones...
47
48
Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning
Dash knows your context, your team, and your work, so your team can stay organized, easily find and share knowledge, and keep projects secure, all from one place.
49
50
51
TensorFlow r1.4
TensorFlow release 1.4 is now public - and this is a big one! So we're happy to announce a number of new and exciting features we hope everyone will enjoy.
52
53
54
How Adversarial Attacks Work
Emil Mikhailov is the founder of XIX.ai [http://XIX.ai] (YC W17). Roman Trusov is a researcher at XIX.ai. Recent studies by Google Brain have shown that any machine learning classifier can be tricked to give incorrect predictions, and with a little bit of skill, you can get them to give pretty much any result you…
55
56
Fooling Neural Networks in the Physical World with 3D Adversarial Objects
We've developed an approach to generate 3D adversarial objects that reliably fool neural networks in the real world, no matter how the objects are looked at.
57
58
Eager Execution: An imperative, define-by-run interface to TensorFlow
Posted by Asim Shankar and Wolff Dobson, Google Brain Team UPDATE, April 3 2018: Please see the official user guide for more up-to-date code examp...
59
An Introduction to Deep Learning for Language Modeling
Language models assign probability values to sequences of words. Those three words that appear right above your keyboard on your phone that try to predict the next word you’ll type are one of the uses of language modeling. In the case shown below, the language model is predicting that “from”, “on” and “it” have a high…
60