ethack + computer   34

Data Structures and Algorithms (DSA): The Intuitive Guide | Interview Cake
No confusing academic jargon or proofs. That stuff doesn't help you really get it. Instead, we'll give you a visual, intuitive sense for how data structures and algorithms actually work.

This is not a freaking textbook. No confusing academic jargon. No long lists of properties to memorize. No proofs. Because that stuff doesn't help you actually get it.

Here's what does: Learning what to picture in your head when you think of a dynamic array or a hash map. Learning how to think in algorithms.

That's what this guide is focused on—giving you a visual, intuitive sense for how data structures and algorithms actually work.

So if you've got a big coding interview coming up, or you never learned data structures and algorithms in school, or you did but you're kinda hazy on how some of this stuff fits together...

This guide will fill in the gaps in your knowledge and make you say, "Oooh, that's how that works."

We'll walk you through it all, step by step. Starting from the beginning.
computer  science  algorithm  book 
october 2017 by ethack
Practical Deep Learning For Coders—18 hours of lessons for free
Welcome to's 7 week course, Practical Deep Learning For Coders, Part 1, taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down. Oh and one other thing... it's totally free!
machine-learning  artificial-intelligence  computer  science  course 
july 2017 by ethack
Home Page of Geoffrey Hinton
Basic papers and papers on deep learning without much math
machine-learning  computer  science 
july 2017 by ethack
Classic papers
Scholarly research is often about the latest findings - the newest knowledge that our colleagues have gleaned from nature. Some articles buck this pattern and have impact long after their publication. Today, we are releasing Classic Papers, a collection of highly-cited papers in their area of research that have stood the test of time. For each area, we list the ten most-cited articles that were published ten years earlier. This release of classic papers consists of articles that were published in 2006 and is based on our index as it was in May 2017. To browse classic papers, select one of the broad areas and then select the specific research field of your interest. For example, Agronomy & Crop Science, Oil, Petroleum & Natural Gas, and African Studies & History. The list of classic papers includes articles that presented new research. It specifically excludes review articles, introductory articles, editorials, guidelines, commentaries, etc. It also excludes articles with fewer than 20 citations and, for now, is limited to articles written in English.
curated  math  computer  science  machine-learning 
june 2017 by ethack
openai/retro - Retro Games in Gym
A software platform for evaluating and training intelligent agents across the world’s supply of games, websites and other applications.

Agents use the same senses and controls as humans: seeing pixels and using a keyboard and mouse. Universe makes it possible to train a single agent on any task a human can complete with a computer.
artificial-intelligence  computer  science 
december 2016 by ethack
The Open Source Data Science Masters
The open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to making use of data.
computer  science  course 
august 2016 by ethack
vhf/free-programming-books: Freely available programming books
This list initially was a clone of stackoverflow - List of Freely Available Programming Books by George Stocker. Now updated, with dead links gone and new content.
book  curated  computer  science 
august 2016 by ethack
OpenAI Gym
Open source interface to reinforcement learning tasks.

The gym open-source project provides a simple interface to a growing collection of reinforcement learning tasks. You can use it from Python, and soon from other languages.
computer  science  math  machine-learning 
may 2016 by ethack
TensorFlow -- an Open Source Software Library for Machine Intelligence
TensorFlow is an Open Source Software Library for Machine Intelligence
library  math  computer  science  machine-learning 
may 2016 by ethack
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
transparent use of a GPU – Perform data-intensive calculations up to 140x faster than with CPU.(float32 only)
efficient symbolic differentiation – Theano does your derivatives for function with one or many inputs.
speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
dynamic C code generation – Evaluate expressions faster.
extensive unit-testing and self-verification – Detect and diagnose many types of errors.
python  library  math  computer  science  machine-learning 
may 2016 by ethack
Machine Learning and Computer Science
blog  math  computer  science 
march 2016 by ethack
Deep Learning
Artificial intelligence and machine learning
computer  science  algorithm 
february 2016 by ethack
40 Key Computer Science Concepts Explained In Layman’s Terms
Computer science summarized in an article. For everyone. Contains minimal technical terms and jargons.
computer  science  security  math 
december 2015 by ethack
The Algorithmist is a resource dedicated to anything algorithms - from the practical realm, to the theoretical realm. There are also links and explanation to problemsets.
algorithm  computer  science 
september 2015 by ethack
A gallery of interesting IPython Notebooks
This page is a curated collection of IPython notebooks that are notable for some reason. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there.
science  computer  math 
may 2015 by ethack
Learn To Code
Learn to code interactively, for free.
learning  computer  science 
march 2015 by ethack
Competitive Programming
This is the supporting web page for a book titled: "Competitive Programming 3: The New Lower Bound of Programming Contests" written by Steven Halim and Felix Halim.
book  algorithm  computer  science 
march 2015 by ethack
Assignments — Problem Solving with Algorithms and Data Structures
An interactive version of Problem Solving with Algorithms and Data Structures using Python.
python  learning  computer  science 
february 2015 by ethack
Help us find and tag all free programming learning resources
curated  learning  computer  science 
february 2015 by ethack
Hacker's guide to Neural Networks
My personal experience with Neural Networks is that everything became much clearer when I started ignoring full-page, dense derivations of backpropagation equations and just started writing code. Thus, this tutorial will contain very little math (I don't believe it is necessary and it can sometimes even obfuscate simple concepts). Since my background is in Computer Science and Physics, I will instead develop the topic from what I refer to as hackers's perspective. My exposition will center around code and physical intuitions instead of mathematical derivations. Basically, I will strive to present the algorithms in a way that I wish I had come across when I was starting out.

"...everything became much clearer when I started writing code."

You might be eager to jump right in and learn about Neural Networks, backpropagation, how they can be applied to datasets in practice, etc. But before we get there, I'd like us to first forget about all that. Let's take a step back and understand what is really going on at the core. Lets first talk about real-valued circuits.
computer  science  machine-learning 
february 2015 by ethack
Papers We Love is a community built around reading, discussing and learning more about academic computer science papers. This repository serves as a directory of some of the best papers the community can find, bringing together documents scattered across the web.

Due to licenses we cannot always host the papers themselves (when we do, you will see a :scroll: emoji next to its title in the directory README) but we can provide links to their locations.

If you enjoy the papers, perhaps stop by a local chapter meetup and join in on the vibrant discussions around them.
curated  computer  science 
january 2015 by ethack
Open Data Structures
Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs.

Data structures presented in the book include stacks, queues, deques, and lists implemented as arrays and linked-lists; space-efficient implementations of lists; skip lists; hash tables and hash codes; binary search trees including treaps, scapegoat trees, and red-black trees; integer searching structures including binary tries, x-fast tries, and y-fast tries; heaps, including implicit binary heaps and randomized meldable heaps; graphs, including adjacency matrix and ajacency list representations; and B-trees.

The data structures in this book are all fast, practical, and have provably good running times. All data structures are rigorously analyzed and implemented in Java and C++. The Java implementations implement the corresponding interfaces in the Java Collections Framework.

The book and accompanying source code are fre
learning  python  java  c++  computer  science 
january 2015 by ethack
The AI Programmer's Bookshelf
A list of useful books for game AI programming.

Artificial Intelligence
Machine Learning
Natural Language Processing
learning  computer  science 
january 2015 by ethack
Awesome Computer Science Courses
List of awesome university courses for learning Computer Science!
learning  computer  curated  science 
december 2014 by ethack
This is a book about Natural Language Processing. By "natural language" we mean a language that is used for everyday communication by humans; languages like English, Hindi or Portuguese. In contrast to artificial languages such as programming languages and mathematical notations, natural languages have evolved as they pass from generation to generation, and are hard to pin down with explicit rules. We will take Natural Language Processing — or NLP for short — in a wide sense to cover any kind of computer manipulation of natural language. At one extreme, it could be as simple as counting word frequencies to compare different writing styles. At the other extreme, NLP involves "understanding" complete human utterances, at least to the extent of being able to give useful responses to them.

Technologies based on NLP are becoming increasingly widespread. For example, phones and handheld computers support predictive text and handwriting recognition; web search engines give access to information locked up in unstructured text; machine translation allows us to retrieve texts written in Chinese and read them in Spanish; text analysis enables us to detect sentiment in tweets and blogs. By providing more natural human-machine interfaces, and more sophisticated access to stored information, language processing has come to play a central role in the multilingual information society.

This book provides a highly accessible introduction to the field of NLP. It can be used for individual study or as the textbook for a course on natural language processing or computational linguistics, or as a supplement to courses in artificial intelligence, text mining, or corpus linguistics. The book is intensely practical, containing hundreds of fully-worked examples and graded exercises.

The book is based on the Python programming language together with an open source library called the Natural Language Toolkit (NLTK). NLTK includes extensive software, data, and documentation, all freely downloadable from Distributions are provided for Windows, Macintosh and Unix platforms. We strongly encourage you to download Python and NLTK, and try out the examples and exercises along the way.
learning  python  computer  science 
december 2014 by ethack

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