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New
The latest blog posts to take off on Hacker News. 6,336 posts.
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Teaching Robots to Understand Semantic Concepts
Posted by Sergey Levine, Faculty Advisor and Pierre Sermanet, Research Scientist, Google Brain TeamMachine learning can allow robots to acquire com...
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Interpreting neurons in an LSTM network
A few months ago, we showed how effectively an LSTM network can perform text transliteration .
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The future of deep learning
This post is adapted from Section 3 of Chapter 9 of my book, Deep Learning with Python (Manning Publications).
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Robust Adversarial Examples
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
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Facets: An Open Source Visualization Tool for Machine Learning Training Data
Posted by James Wexler, Senior Software Engineer, Google Big Picture Team(Cross-posted on the Google Open Source Blog)Getting the best results out ...
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The Limitations of Deep Learning
This post is adapted from Section 2 of Chapter 9 of my book, Deep Learning with Python (Manning Publications).
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Machine Learning Crash Course: The Bias-Variance Dilemma
Machine Learning at Berkeley is a student organization at UC Berkeley. Click to read ML@B Blog, by Machine Learning at Berkeley, a Substack publication with thousands of subscribers.
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End to End Machine Learning Pipeline Tutorial
In the fall of 2016, I was a Teaching Fellow (Harvard's version of TA) for the graduate class on "Advanced Topics in Data Science (CS209/109)" at Harvard University. I was in-charge of designing the class project given to the students, and this tutorial has been built on top of the project I designed for the class.
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What if AI is a failed dream?
A collection of thoughts, ideas and unsolicited opinion from Made by Many: articles, reflections, talks and interviews from the people doing the work.
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Using Deep Learning to Create Professional-Level Photographs
Posted by Hui Fang, Software Engineer, Machine PerceptionMachine learning (ML) excels in many areas with well defined goals. Tasks where there exis...
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Using Tesseract OCR with Python
In this tutorial you will learn how to apply Optical Character Recognition (OCR) to images using PyTesseract, Python, and OpenCV.
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When not to use deep learning
I know it’s a weird way to start a blog with a negative, but there was a wave of discussion in the last few days that I think serves as a good hook for some topics on which I’ve been thinking recently. It all started with a post in the Simply Stats blog by Jeff Leek on the caveats of using deep learning in the small…
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Benchmarking TensorFlow on Cloud CPUs: Cheaper Deep Learning Than Cloud GPUs
Using CPUs instead of GPUs for deep learning training in the cloud is cheaper because of the massive cost differential afforded by preemptible instances.
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Where machine learning meets rule-based systems
Summary: This post addresses some high-level questions like: Longer term, how much of the verification of Intelligent Autonomous Systems can be done with just Machine Learning (ML)? Should most req…
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Generating faces of cats using Generative Adversarial Networks
I experimented with generating faces of cats using Generative adversarial networks (GAN). I wanted to try DCGAN, WGAN and WGAN-GP in low and higher resolutions. I used the CAT dataset (yes this is …
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An Adversarial Review of “Adversarial Generation of Natural Language”
Or, for fucks sake, DL people, leave language alone and stop saying you solve it.
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I ask 100 information questions to four digital assistants
After seeing the poor feedback of Watson in Bridge Crew, I decided to take my four digital assistants for a spin. After 21 questions across four assistants, I learned that Alexa cannot give basic information about Amazon Prime videos, none of them can properly understand which movie you're looking f
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Generating music with expressive timing and dynamics
We present Performance RNN, an LSTM-based recurrent neural network designed to model polyphonic music with expressive timing and dynamics. Here’s an example...
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How HBO’s Silicon Valley Built “Not Hotdog” with TensorFlow, Keras and React Native
How we beat the state of the art to build a real-life A.I. app.