Dawny33

13 ms·

91.
Professor Beekums' Software Lessons(professorbeekums.com)
1 points by Dawny33 10y ago|0 comments
92.
AI Web Development(github.com)
2 points by Dawny33 10y ago|0 comments
93.
Fighting Ebola with JavaScript(medium.com)
1 points by Dawny33 10y ago|0 comments
94.
Why is machine learning ‘hard’?(ai.stanford.edu)
327 points by Dawny33 10y ago|88 comments
95.
by Dawny33 10y ago
Wow! I thought it's Apache Hive. We're officially out of names :(
96.
by Dawny33 10y ago
Took me an entire 3 paragraphs to realise it's not Apache Spark :D But, an impressive inter-project post!
97.
Apache Kafka 0.10.1.0(confluent.io)
2 points by Dawny33 10y ago|0 comments
98.
Principles of Chaos Engineering(principlesofchaos.org)
2 points by Dawny33 10y ago|0 comments
99.
Distributed Algorithms [pdf](users.ics.aalto.fi)
2 points by Dawny33 10y ago|0 comments
100.
Getting Real About Distributed System Reliability(blog.empathybox.com)
1 points by Dawny33 10y ago|0 comments
101.
List of project ideas for distributed systems(scs.stanford.edu)
2 points by Dawny33 10y ago|0 comments
102.
Beyond Deep Learning – 3rd Generation Neural Nets(datasciencecentral.com)
2 points by Dawny33 10y ago|1 comments
103.
by Dawny33 10y ago
I find it quite legit. Having a singe repo would hinder progress as more and more implementations are added. Even JuliaStats followed a similar distribution ( https://github.com/JuliaStats ), and it looks neat and organized.
104.
Concise Visual Summary of Deep Learning Architectures(datasciencecentral.com)
3 points by Dawny33 10y ago|0 comments
105.
Pyflame: Uber's Ptracing Profiler for Python(eng.uber.com)
4 points by Dawny33 10y ago|0 comments
106.
How Google Code Search Worked(swtch.com)
2 points by Dawny33 10y ago|0 comments
107.
Regular Expression Matching Can Be Simple and Fast(swtch.com)
4 points by Dawny33 10y ago|0 comments
108.
Deep Neural Networks for YouTube Recommendations [pdf](static.googleusercontent.com)
1 points by Dawny33 10y ago|0 comments
109.
Telling stories with data using the grammar of graphics(codewords.recurse.com)
3 points by Dawny33 10y ago|0 comments
110.
Phd level ML courses(reddit.com)
3 points by Dawny33 10y ago|0 comments
111.
Surviving Data Science at the speed of hype(john-foreman.com)
2 points by Dawny33 10y ago|0 comments
112.
Should you reset your CSS?(sixrevisions.com)
2 points by Dawny33 10y ago|0 comments
113.
Recommending music on Spotify with deep learning(benanne.github.io)
4 points by Dawny33 10y ago|0 comments
114.
Implementing a research paper(codecapsule.com)
2 points by Dawny33 10y ago|0 comments
115.
How do you implement a research paper?(reddit.com)
1 points by Dawny33 10y ago|0 comments
116.
FastText – Library for fast text representation and classification(github.com)
220 points by Dawny33 10y ago|52 comments
117.
by Dawny33 10y ago
One doc to learn them all :) The `Optimizing and debugging code` part is where most data scientists falter. So, this is a really nice effort for bringing it all together!
118.
by Dawny33 10y ago
Corresponding [Reddit discussion]( https://www.reddit.com/r/MachineLearning/comments/4tsi2n/uda... ) and [Medium post]( https://medium.com/self-driving-cars/udacitys-self-driving-c... )
119.
Udacity Launches the “Self-Driving Car Engineer Nanodegree”(udacity.com)
4 points by Dawny33 10y ago|2 comments
120.
by Dawny33 10y ago
Super awesome! The gif can do with a better Atom theme, though! The dark one isn't really ideal for a cell-by-cell Jupyter-like implementation

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