121
Fast and Accurate Document Detection for Scanning
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.
New
The latest blog posts to take off on Hacker News. 250 posts.
122
Image Kernels explained visually
An image kernel is a small matrix used to apply effects like the ones you might find in Photoshop or Gimp, such as blurring, sharpening, outlining or embossing. They're also used in machine learning for 'feature extraction', a technique for determining the most important portions of an image. In this context the…
123
Understanding gradient descent
Gradient descent is a standard tool for optimizing complex functions iteratively within a computer program. Its goal is: given some arbitrary function, find a minumum. For some small subset of functions - those that are convex - there's just a single minumum which also happens to be global. For most realistic…
124
AI is always less impressive than is claimed
プログラミングでは開発言語を用いて、アプリやWebサービス、業務システムなどを動かす仕組みを作ります。 将来プログラマーとして働きたいなら、まずは開発言語の基本から理解し、手を動かしながら学ぶことが近道です。 本サイトでは、初心者がつまずきやすい点を先回りして解消しつつ、焦らず段階的にスキルを積み上げる学習手順、職業訓練校の活用方法、復習や制作のコツまで具体的に整理して紹介します。
125
Special projects
Impactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter.
126
Gradient Boosting Explained in 3D
Understanding gradient boosting with interactive 3d-demonstrations
127
128
129
130
131
A Beginner's Guide to Understanding Convolutional Neural Networks
Engineering at Forward | UCLA CS '19
132
133
Introducing Google Cloud Natural Language API, Speech API and New Data Center
Following our announcements from GCP NEXT in March, we’re excited to share updates about Cloud Platform expansion and machine learning . Today we’re launching two new Machine Learning APIs into open beta and expanding our footprint in the United States.
134
Windows Hello face recognition is vulnerable to the Jedi mind trick
Practical development throughout the evolution of Windows.
135
136
Bayesian Machine Learning Explained
So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know …
137
138
A Proposal For the Dartmouth Summer Research Project on A.I. (1955)
A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE
139
Generating Recommendations at Amazon Scale with Apache Spark and Amazon DSSTNE
In this post, I discuss an alternate solution; namely, running separate CPU and GPU clusters, and driving the end-to-end modeling process from Apache Spark.
140
Simple questions to help reduce AI hype
AI is a young field full of amazing potential. This mystery and lack of understanding allows for hype to grow unchecked.
141
The Problem with Reality
Discover all the Meta Quest VR entertainment experiences. Enjoy streaming your favorite VR movies, tv shows, and live sport events. With VR, your living room is better than the theater.
142
143
Heavy Metal and Natural Language Processing
Edit: Part 2 of this post can be found here , and part 3 here . Most of the code used in these posts is available here .
144
145
Wide and Deep Learning: Better Together with TensorFlow
Posted by Heng-Tze Cheng, Senior Software Engineer, Google ResearchThe human brain is a sophisticated learning machine, forming rules by memorizing...
146
We built voice modulation to mask gender in technical interviews
We built software to mask gender in technical engineering interviews. See what we learned and why it matters.
147
How to Start Learning Deep Learning
Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it.
148
149
AI, Apple and Google
As machine learning starts working, how does that change Google, Apple and the smartphone interface? What companies get reshaped around machine learning?
150
Concrete AI Safety Problems
We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.