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My Favorite Deep Learning Papers of 2017
Here are five deep learning papers I felt rose above the rest in 2017.
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The latest blog posts to take off on Hacker News. 121 posts.
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Learning from Scratch by Thinking Fast and Slow
Reinforcement Learning
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Keras Tutorial – Traffic Sign Recognition
In this tutorial Tutorial assumes you have some basic working knowledge of machine learning and numpy., we will get our hands dirty with deep learning by solving a real world problem.The problem we...
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A Zero-Math Introduction to Markov Chain Monte Carlo Methods
For many of us, Bayesian statistics is voodoo magic at best, or completely subjective nonsense at worst. Among the trademarks of the…
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Keras and deep learning on the Raspberry Pi
In this guide you'll learn how to perform real-time deep learning on the Raspberry Pi using Keras, Python, and TensorFlow.
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Building a Poor Man’s Deep Learning Camera in Python
Streaming a webcam to a deep learning model the hacker’s way
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Understanding LSTM Networks (2015)
Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.
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How to break a Captcha system in 15 minutes with Machine Learning
Let’s hack the world’s most popular Wordpress CAPTCHA Plug-in
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An Interactive Introduction to Quantum Computing
This article was originally written in 2014, but has had some minor improvements in December 2017 and January 2018 when I received some useful feedback after the article had unexpectedly been posted on Hacker News in December 2017.
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Photo-realistic lip-sync from text
We present ObamaNet , the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Contrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and does not rely on any traditional computer graphics methods. More…
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Neural Network on a Commodore 64 (1987)
Neural Network on a Commodore 64
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Flare: Clojure Dynamic Neural Net Library
I wrote Flare, a Dynamic Neural Net library in Clojure.
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Neural Networks in JavaScript with Deeplearn.js
An extensive tutorial on how to implement a neural network in JavaScript with deeplearn.js to perform machine learning in JavaScript. You can bring neural networks to your browser by using the performance benefits of WebGL. Deeplearn.js makes it possible to implement a WebGL powered neural network ...
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Deep image prior 'learns' on just one image
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is…
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The impossibility of intelligence explosion
In 1965, I. J. Good described for the first time the notion of “intelligence explosion”, as it relates to artificial intelligence (AI):
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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...
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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...
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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
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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…
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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...
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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…
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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…